456. Improve Critical Thinking

Students with strong critical thinking skills can make better decisions that have real consequences on their quality of life. In this episode, Michael Bishop joins us to discuss an approach to developing these skills that appears to have a persistent effect on students’ ability to avoid common cognitive biases.

Michael is a professor of philosophy at Florida State University. His research interests focus on two areas: rationality (how should we think about important things) and happiness (what is lasting happiness, and how can we get it). He is also a co-author of a recent study investigating the impact of critical thinking classes on common cognitive biases.

Show Notes

Transcript

John: Students with strong critical thinking skills can make better decisions that have real consequences on their quality of life. In this episode, we explore an approach to developing these skills that appears to have a persistent effect on students’ ability to avoid common cognitive biases.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guest today is Michael Bishop. He is professor of philosophy at Florida State University. His research interests focus on two areas: rationality (how should we think about important things) and happiness (what is lasting happiness, and how can we get it). He is also a co-author of a recent study investigating the impact of critical thinking classes on common cognitive biases. Welcome, Michael.

Michael: Thank you. Glad to be here.

John: Thank you for joining us. Today’s teas are:… Michael, do you have tea by any chance?

Michael: No, not today. I usually drink tea in my office, and when I do, it’s usually a Chinese black tea.

Rebecca: Always a nice choice. Today I have an English breakfast tea, nothing that exciting,

John: And I just have nothing that exciting either, a Lady Grey today.

Rebecca: We got to step up our game, John. So, Michael, we’ve invited here today to discuss the field experiment that is described in your co-authored paper entitled “Critical Thinking Classes Can Reduce Common Biases: Results from a Field Experiment.” But before we discuss the study, can we start a little bit with the concept of critical thinking? You teach critical thinking. What are you trying to teach? And what makes someone a good critical thinker?

Michael: Yeah, I think that’s a great question. One of my favorite sayings is the philosopher George Santayana said that a fanatic is someone who, having lost sight of their goals, redoubles their efforts. And so, in teaching critical thinking, I think it’s important to start with an idea of what you want to teach. And so there’s a lot of possible answers. Some people think that a good critical thinker is someone who uses certain rational methods or rules like logic or probability for the scientific method. Other people think that a rational thinker would be an expert debater, someone who can detect fallacies and build a coherent case for their views. Other people view good critical thinkers as virtuous thinkers; someone who is careful, open-minded, curious, intellectually honest, intellectually humble. And I actually think these views are either wrong… so I think that good debaters are often poor reasoners… or incomplete. They capture part of good reasoning, but not everything. So basically, what I am trying to teach in my classes is teaching people to ask good questions. Ask what I call fruitful questions, questions that help us get better answers, not killer questions that end debates, but questions that, if you’re trying to answer them, help get us better answers. So when you view that as the goal, how you go about teaching critical thinking becomes much easier.

John: And as you suggested, people have many different descriptions of what critical thinking is. I know whenever we talked about the importance of this in our Gen Ed program, everyone agrees that critical thinking is extremely important, but they all disagree as to what exactly it is. So it’s helpful, I think, to start with that statement. In this experiment, you’re focused on five common biases. Can you describe the biases that were selected for this study and provide some brief examples of those biases for our listeners?

Michael: Sure. The way psychologists think about these is in terms of biases. The way I think about them is in terms of cheat codes. So there are certain cheat codes people use in debating or in arguments, and cheat code arguments are arguments that you can use to defend pretty much any view you want. And so cheat codes are really tools of manipulation, more than tools of trying to figure out what the truth is or what a good decision is. And so when you think about good critical thinkers as thinkers who can ask good questions, the way I think about it is when you detect a cheat code, when you detect somebody using a cheat code on you, you should be able to ask them a question that diffuses the cheat code and points them in better directions. So let me tell you some of these cheat codes. One I call the focalizing cheat code, and it might go something like this: We should go to war because it has certain benefits, so, if an argument like that, or we should not go to war because it has certain costs. If you’re convinced by an argument like that, I think that you’ve fallen for a cheat code, and the best way to see this is to consider an example where where it’s clear that it’s a cheat code. So, suppose I gave you two envelopes, and I said you can look in these envelopes, and you can take all the money that’s in the envelope that you choose, but you can only choose one envelope. Suppose I open one envelope, see that there’s $5 and I take it without looking in the second envelope. In that case, you’d think that I’ve made a mistake, that I haven’t reasoned wisely about this particular decision because I didn’t look in the second envelope. If I say, “Look, the reason I chose the first envelope is that having $5 is better than not having $5.” It’s true that having $5 is better than not having $5, so there is an advantage to choosing envelope one. But when you think about it, what makes a choice a good one isn’t that it makes you better off than you are now. What makes a choice a good one is that it makes you better off than what else you might have done. Now, economists call this opportunity cost, as I suspect you guys know. And so, one thing I’m trying to do is get my students to, when they see someone argue for, “Hey, we should go to war because it’s going to bring us benefits.” I want them to at least stop and be able to ask the question, wait a minute, what are some alternatives to going to war? Because even if it’s true that going to war brings us benefits, and we can argue about that, but even if it’s true, it might not be the best thing to do, in the same way that choosing envelope one, even though it brings us benefits, might not be the smart thing to do. So I call that the focalizing cheat code. Another cheat code that I teach is what you might call the correlation cheat code. So I say, “Hey, a new healthcare plan is passed, and healthcare costs have gone up 7% a year since this plan is passed. Boy, this plan really stinks. It’s caused our healthcare costs to go up a lot.” This again, the question isn’t is the conclusion right in the war case or in this case, the question is: Is this a cheat code argument? And it is. And the reason is, what you don’t know, and what you really need to know, is how high would have costs been had this healthcare plan not been in place. So again, I say this healthcare plan is great or it’s bad, and I point to something that happened afterwards that. That’s what I call the correlation cheat code because you’re taking this correlation and you’re taking it as evidence for thinking that this policy has caused this particular result. And so again, what I want people to do is just stop and ask the question: “Well, wait a minute. Maybe without the health care plan, costs would have gone up even more, or maybe they would have gone up just the same, or maybe they’d have gone up less.” But unless you ask the question, it’s easy to get manipulated into accepting views that aren’t true. Another cheat code is the regression cheat code. This is a very common one. So lots of coaches and parents think that punishment is effective at molding behavior. I know that when I played sports as a youth, my coaches thought that after a poor performance, if you punish the players by making them work really hard in practice, they’ll do better next time. And sure enough, after a poor performance and after we were punished, we did tend to perform better. But again, the question is: “Did the punishment do that, or was it just a return to our normal level of performance?” So that’s the regression cheat code, rather than just saying, “Hey, maybe you’re returning to your normal level of performance,” saying that “it’s the result of a punishment or the result of a reward.” The third one is sunk cost cheat code, another one that’s a favorite of economists, defending a decision because of the resources already spent on it, so let’s stick with this policy or this war because of sunk costs. Those sunk costs are gone; they’re gone no matter what you do. So you might as well do what’s best for your future. And the last one that we looked at was the gambler’s fallacy, and that is basically treating statistically independent events, as if they were statistically dependent. So, if a coin comes up five times heads, and it’s really a fair coin, thinking that it’s more likely to come up tails next time. So, those were the five cheat codes we looked at.

Rebecca: I think the soundtrack that we have going on with the thunder is really emphasizing that these are cheat codes. [LAUGHTER]

Michael: Indeed, indeed. [LAUGHTER]

Rebecca: It’s very ominous. [LAUGHTER] Can you talk a little bit about how you chose these particular cheat codes?

Michael: Sure. So this goes back to my view about good reasoning. So 20 years ago or so, me and a friend defended a theory of what rational reasoning was, and what we said was that the good reasoning is reasoning that’s cheap, it’s accurate, and it’s focused on significant problems. And so the way I chose these cheat codes is because I thought that they were significant. That is, people use them to manipulate you when it comes to really important issues. So I see these cheat codes a lot out there being used in the world for really important issues. And the cheap part came in because I think that in thinking about how to teach critical thinking, I think one thing you want to do as a critical thinker is to diagnose the problem. Why are my students thinking badly when they do think badly? And I think there’s three general answers that people tend to think about, although often this doesn’t get talked about enough. So one reason students might be thinking badly is that they don’t know these rules, so it might be an ignorance problem. And if it’s an ignorance problem, then it’s going to be really hard to teach them to use these rules in a way that they keep using the rules after they leave the class. Because not only do they have to learn the rules and learn to apply the rules, but when they leave your class, they need to stop thinking in their old ways and use these rules, which tend to be hard to remember, hard to apply in the real world. And so, one reason that I think studies have not been successful or have had limited success in teaching people to reason well in a way that sticks once they leave the classroom, is that too often we teach critical thinking as if the problem is an ignorance problem. So we need to teach our students new rules. So one reason I chose these rules is that I speculated that they already knew these rules and they sometimes applied them correctly. And if that’s right, if they sometimes apply these rules correctly, then I don’t need to teach them a new rule, and I don’t need to teach them how to apply the rule. They’re already doing that. All I need to do is get them to see that it’s the right rule, and then ask them, “Hey, why aren’t you using it in these cases over here, where it seems like you should still be using the rules.” So, for example, think about the two envelope problem that I talked about before. None of my students think, “Oh, yeah, choose the $5, that’s the way to go. None of them think that. They know perfectly well that the way you evaluate that choice is not in terms of “Hey I’m $5 better off than I am now,” it’s “Hey if there’s $1,000 in the other envelope, then I’m 995 dollars worse off than if I’d have chosen envelope number two.” They all see that, so I don’t have to teach them any new rules. All I have to do is when I say “Hey let’s go to war, it has this advantage,” and if they say, “Yeah, that sounds good,” I can say, “Wait a minute. How is this different from the two envelope problem?” So the main reason I chose these, and these aren’t the only rules I teach in my critical thinking class, but the main reason I choose the rules that I do is that I think that my students already know these rules and at least sometimes apply them, and so when I introduce them to these rules in cases to which they are inclined to apply them, they give me what I call the old bovine stare. They look at me like, “Well, that’s obvious. You’re not teaching me anything I didn’t already know.” And that’s what I want. I want them when I introduce these rules to say, “I already knew that. Why are you teaching me something so obvious?” Because then the next class period, or a week from now, or a month from now, when I introduce an argument where they ought to be using this rule that they said was so obvious and they’re not, I don’t have to say, “Hey, you guys have made a mistake.” I can just ask them, “Okay, wait a minute, you said this rule was obvious, why aren’t you applying it here? Either you were wrong on day one about that rule being the right rule, or you’re wrong right now in not applying that rule.” And so there’s a real sense in which the reason I chose these rules is because I think I’m really not teaching my students anything new. So here’s one way to think about it: rather than teaching them like calculus, like a chain rule, like a new formula that they don’t know, what I’m really trying to do is get them to recognize cardinals. They already know what a cardinal looks like, so I’m just trying to get them to recognize cardinals, and so that’s why I think that the way I teach critical thinking tends to work and is way easier than starting off with the assumption that “Hey, my students don’t know anything, and I need to teach them a whole bunch of new rules.” So that’s why I chose these rules, and that’s why I choose the rules that I use in my critical thinking class.

John: And these are rules that, when you think about them as you framed it, they’re pretty obvious. But in practice, they’re rules that people violate all of the time in their decision making. What really impressed me about the study was that you did get some [LAUGHTER] results out of this because so many studies of behavior modification have been found to be ineffective beyond a very short period of time, often not lasting through the end of the class.

Michael: That’s absolutely right. In fact, one of my old professors, he said this about logic classes, but I think it unfortunately is often true of critical thinking classes, but he said that logic classes teach you to do one and only one thing, and that’s take logic tests. And I sometimes worry that that’s the case with a lot of our attempts to teach people to think better, is that what we’re really doing is teaching them to take tests because they will use the rules on the test. But if you want them to think about this afterwards, I really do think that the main thing is to get them to commit to the rule, and then get them to recognize, “Hey, wait a minute,” to be sensitive to when they see it in the real world because it’s all over the place. Once you get sensitive to it, you start seeing it everywhere. And indeed, one danger of my class, of my critical thinking class that I talk to my students is what I call “the turd in the punch bowl problem,” and that is being a good critical thinker isn’t necessarily going to make you popular, and so you need to be careful about when you point out to people that their arguments aren’t very good, because if you do it too much, you will end up being like the turd in the punch bowl at a party. People will look at you and say, “Oh, I’m going to stay away from that person.” And so, at the end of the semester, I will ask them, “Have you had any turd in the punch bowl experiences as a result of this class? And at the end of the semester, a lot of the students – and I don’t know if this is good or bad – but about half of the cases involve the sunk cost principle, and usually it involves the destruction of a relationship. So people will end a relationship because the only reason they’ve been sticking with the relationship is all the pain and effort and heartache they’ve put into the relationship.

John: And I know behavioral economists have noted that quite a bit. That once they really get this message through, it leads to a breakup of so many relationships of their students. But even people who know these things sometimes fall into these traps. And one example I think I mentioned on the podcast before, and I know I’ve mentioned it to Rebecca, is one time I was at a conference with a few other people, and we went over to Universal park, and it was just really hot that day, it was like 97 degrees, and we had spent quite a bit for the tickets for the day, and we were just kind of miserable. And one of the other people said we should just go. And my response to a historian was that we spent $230 and we’ve gone on two rides. We really should stay and get our money’s worth. And her response was, “But that’s a sunk cost. It shouldn’t matter,” and it’s easy for us to fall into that.

Michael: Absolutely, and indeed, this is one of the reasons I choose the rules that I do, is that I think many of them are significant. They really can make a difference to the quality of your decisions and the quality of your judgments.

Rebecca: Can you talk a little bit about the two-stage training program that was used in class?

Michael: Sure. So I gave you a little bit of an example of it already, but let me give you an example with another case with the correlation cheat code. So my go-to example here is I’ll tell the students, “Well, you know, yesterday I ate chocolate and today I broke out with zits. Boy, that means that eating chocolate causes zits,” and they immediately say, “No, that’s terrible.” So the first stage, what I’m doing is I’m introducing examples where they are disposed to use the rule. So nobody says, “Yeah, that’s great evidence for thinking that eating chocolate causes zits.” And then we talk a little bit about, “Okay, well, what would count as good evidence?” And they come up pretty quickly with maybe just a little bit of help, “Well, you want to know what happens when you eat chocolate and when you don’t eat chocolate, because what you really want to know is what difference does eating chocolate make to your complexion, and so what you want to know is what happens when you eat chocolate and you don’t eat chocolate, and everything else relevant to your complexion is the same, and what difference is there in these two cases,” and so that’s the first stage. The first stage is just introducing them to these rules, these cheat codes. So I give them the cheat code, but really a cheat code which they detect right away as a cheat code, and that they know as a cheat code. And then I spend the rest of the semester giving them arguments that use that cheat code, but are not as easy to detect. And often these are political arguments, and one reason I don’t get into trouble – at least I haven’t so far – in giving political arguments is that these cheat codes you can use them to defend views on the left, on the right, in the middle. They’re tools of manipulation. It doesn’t matter what the truth is. You can use these cheat codes to defend the true view, the false view, smart decisions, dumb decisions. And so, what I’m really interested in doing is getting them to be able to detect these cheat codes. So a first step is just introduce them to these cheat codes in a way that they will be disposed to accept and think is obvious. And then after that, I’ll give them lots of examples in which they’re, at least many of them, are not disposed to think is obvious. And then rather than me sort of coming from on high and saying, “Well, you know, you made a mistake there, dummy.” I just step back and I say, “Wait a minute. We seem to have an inconsistency here. You insisted that this rule was right on Monday, and on Friday you’re not using the rule in this case. What’s going on?” And I have yet to have a student, and I’ve taught 1000s of students, I’ve yet to have a student say, “Okay, I’m going to rethink that rule.” They all say, “Oh, well, I made a mistake here. I reasoned badly,” and by the end of the semester, they’re not reasoning badly.

Rebecca: Sounds like you spend a very small amount of time on stage one and significant time on stage two. Do you do all of the cheat codes at the beginning of the semester and then work on utilizing them throughout the semester, or do you kind of do units on different cheat codes? How do you organize?

Michael: So I will introduce a cheat code and then I’ll give them examples using that cheat code and all the cheat codes we’ve covered. So once a cheat code is introduced, they might see it at any point in the future, and I think it’s really important they keep practicing it and keep being able to recognize it, and not just think, “Hey, I know this is the cheat code we’re working on today, so I just need to be on the lookout for that one.” Another thing I do, and I think this is really important, is I will sometimes give them arguments that are promising, that if you grant the truth of the premises, they at least give you some reason to accept the conclusion. Because I don’t want the students just to be skeptical of all arguments. I want them to be able to distinguish between “Okay, that’s a cheat code. That’s definitely a bad argument. The conclusion might be true, the conclusion might be false, but that’s not a good reason to accept anything” and then “Okay, well, what would count as good evidence for thinking that this is a smart decision or that this policy has led to increased costs or decreased costs?” And I want them to be able to say, “Okay, look, that argument right there, that’s pretty promising. This argument over here definitely uses a cheat code.”

John: So you start off with a little bit of retrieval practice with some applications of the same concept, and then you move into some interleaved practice, which raises the cognitive difficulty just a little bit. But it’s one of those desirable difficulties where the more cognitive effort that students have to place in identifying what is at issue, the more deeply they tend to learn it, and the more likely it is that those concepts will stick and also transfer into other applications, which is what you’re building to in this program.

Michael: Absolutely, absolutely, the interleaved practice is really important, and I wouldn’t go back to sort of the blocking practice.

Rebecca: So we’ve talked a little bit about what the intervention is. Can you talk a little bit about how the study was conducted, like the size of the treatment group, the control group, etc.

Michael: Sure. So in the paper, we really describe just the second study we did, we actually did two studies, and in both studies we had almost 400 students. In the first study we had 395 students, and the second study we had 397 students. And some of the students were taking a critical thinking class from me, and the rest of the students were taking other philosophy classes. So in the study that we report on, we had 113 critical thinking students and 284 control students. Then what we did is, in the first week of class, we gave everyone a pretest, and this involved the five cheat codes, and then two good arguments, or at least promising arguments. They weren’t necessarily great, but they didn’t use any cheat codes. And the reason we included these, what we called skepticism checks, was just to check if all we were doing was creating skeptical students rather than students who could distinguish between promising arguments and cheat code arguments. Then, at the end of the semester, we gave them the same survey, same seven questions, and not surprisingly, in both of the studies, the training group students did much better than the control students. In the first study, we waited three months. So basically, the way to think about it is that these classes all happened in the fall. We waited till the end of the spring semester. It might have been four months, and gave them same questions again in the first study. We gave them the same questions again. The trained students did much better, and then we gave them different questions, and they still did much better. So in the second study we did, which is really the only study that we report on in the article, we gave them pre-test at the beginning of the semester, post-test. Then we waited a full academic year plus the spring, so we waited 16 months and only asked them different questions. So the pretest and posttest were the same questions, and again my students did much much better. But then we waited 16 months and asked them seven different questions, and the trained students were still doing much better, detecting the cheat codes and recognizing that the good arguments were still pretty good arguments.

John: DId the trained students do significantly better with all 5 of these cheat codes?

Michael: No, they did better on 4 of the 5 cheat codes. They did better on the correlation, regression, sunk cost, and opportunity cost cheat codes, but in all the studies that we did, on all the surveys that we asked, we just couldn’t get the trained students to do better on the gambler’s fallacy questions.

John: But still, having students do better on 4 out of those 5 cheat codes so long after the initial intervention is a really impressive result. One of the things you talk about near the end of the paper, though, is that you note that there are some possible sample selection issues. Could you just talk a little bit about what source of sample selection might have affected the magnitude of those results?

Michael: So one worry that one might have is that the students were not distributed randomly. So the students chose to take the critical thinking class, the students chose to take other philosophy classes, and so there might be something about the students who chose critical thinking classes that would incline them to be better critical thinkers, despite the fact that on the pre-tests, all the students in both groups did equally badly. So that’s one worry one might have, and the thing to do there is, although it’s not really easy given the institutions of academia, is to randomly distribute students into critical thinking classes and other classes. So that’s the main selection worry. Another reason why we might have gotten good results is that I had those students for 15 weeks. Some interventions talk about full- semester interventions, but often they’re fairly short interventions. I was able to have the students for 15 weeks and made them practice a lot.

Rebecca: It sounds like in the design of the experiment in your class, just generally, there’s lots of opportunities for feedback and correction throughout the semester.

Michael: Yes, absolutely. There’s a lot of feedback, a lot of correction, and I have a big bank of good and bad arguments that I can give them over the course of the semester that use these cheat codes or that don’t use the cheat codes. And one of the things I do for an essay in this class is ask the students to put themselves in the shoes of someone who’s trying to come up with a cheat code argument and ask them to give me bad arguments that use these cheat codes, and they actually turn out to be pretty good at it. And many of the examples that I use on later students are examples that prior students have come up with.

Rebecca: Sounds like it makes it a little bit fun too.

Michael: Yes, I do ask them for any humorous examples or examples that us boomers would have a hard time understanding. [LAUGHTER]

John: One of the things we appreciated about this study is that the data, the results, and the analyses that were done are all reported in more detail on the Open Science Framework website. We’ll include a link to that website in the show notes for this episode.

Rebecca: So we always wrap up by asking, “What’s next?

Michael: A few things are next. One is I’m writing a textbook that uses the materials that I teach. The way I teach critical thinking is different from the way other people teach it. I haven’t found a good textbook that teaches critical thinking the way I like to teach it. So I’m currently writing a textbook that hopefully will help other people get good results. I’m also planning to build, if I can, a game that anybody can play to see whether they can avoid being bamboozled [LAUGHTER] by cheat code arguments, this is sort of a longer term project because writing a book is going to be useful, hopefully, for people to teach this. But what’s really important, in my view, what makes this stick is practicing on lots of examples. And so, in a game where people see an argument and you say, “Well, is this promising argument, or is somebody trying to manipulate you?” we’ll hopefully give people practice at being able to detect those cheat code arguments. So those are two of the projects that I’m working on.

John: On the latter project, will it be a board game? Will it be an electronic game? Will it be an online game, or is that something that’ll be decided further in the future?

Michael: No, it’ll be an online game. Is what I’m hoping to do. I want to do something that can be used not just by my students and that’s easily accessible.

Rebecca: Sounds like some great projects. Thanks for joining us today.

Michael: Oh, you’re welcome. It’s been a pleasure.

John: Thank you. I was really impressed by this study [LAUGHTER] because, as I mentioned before, it’s so rare to see effects that are so significant and persistent. It’s, I think, something that people can learn from in terms of other types of issues where it’s hard to convey this sort of long-term impact on students.

Michael: Well, thank you so much.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

[MUSIC]

453. Prequestions and AI

A growing body of research indicates that asking questions about concepts students have not yet studied increases learning. In this episode, Steven Pan and Judith Schweppe join us to discuss how generative AI can facilitate this technique by generating prequestions.

Steven is an Assistant Professor of Psychology and the Director of the Learning Sciences Lab at the National University of Singapore whose research focuses on evidence-based teaching approaches. Judith is a Professor of Educational Science with a focus on teaching with digital media at the University of Erfurt. Her research focuses on working memory, long-term memory, and learning.

Show Notes

Transcript

John: A growing body of research indicates that asking questions about concepts students have not yet studied increases learning. In this episode, we discuss how generative AI can facilitate this technique by generating prequestions.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guests today are Steven Pan and Judith Schweppe. Steven is an Assistant Professor of Psychology and the Director of the Learning Sciences Lab at the National University of Singapore whose research focuses on evidence-based teaching approaches. Judith is a Professor of Educational Science with a focus on teaching with digital media at the University of Erfurt. Her research focuses on working memory, long-term memory, and learning. Welcome, Judith, and welcome back, Steven.

Steven: Thank you.

Judith: Thank you, thank you for having us,

John: And thank you for agreeing to come on at times that are a little bit challenging. Could you mention for our listeners what time it is where you are?

Steven: I’ll go first. It’s a bit after 4:30 in the morning [LAUGHTER] here in Singapore.

Rebecca: Starting your day right.

Judith: And for me it’s 10:30 pm, so I’m about to go to bed after this recording.

Rebecca: Best way to end the day. [LAUGHTER]

John: Today’s teas are:… Judith, are you drinking any tea?

Judith: Yeah, in fact, I’ve prepared a ginger and lemon tea.

Rebecca: A good one for the evening.

Judith: Yeah, exactly, that was the idea.

Steven: Well, I sometimes drink tea. Actually, I should drink more right now. I just have water.

John: And Rebecca,

Rebecca: I have Golden Monkey today.

John: That’s been a while.

Rebecca: Yeah, it’s been a while.

John: And I have an Irish breakfast tea today.

Rebecca: Awesome. So we invited you both here today to discuss an article in the Journal of Applied Research in Memory and Cognition that you both co-authored with three additional co-authors, entitled “Using ChatGPT Generated Pre-Questions to Improve Memory and Text Comprehension.” But before we discuss your study, it might be helpful to talk a little bit about what is meant by pre-questioning, and what past research has suggested about the impact.

Steven: Well, pre-questioning is a learning technique that involves attempting to guess the answers to questions about information you haven’t formally studied or know much about, and then there is, after you’ve attempted to answer those questions, the opportunity to learn the correct answers from some form of instruction, such as a lecture, watching a video, or, for example, reading a text passage. So you guess first, then you discover the answers afterwards. That’s pre-questioning. It’s similar to, it could you even be considered a part of a class of strategies that sort of involve what you might call pre-instruction learning activities: do something in advance of formal instruction, and pre-questioning, along with those other strategies or techniques, can improve learning in a variety of circumstances, improving, as I mentioned, learning from videos, learning from lectures, learning from text. There are a growing number of studies showing that it has these kinds of benefits.

Judith: What’s remarkable about this is that learners typically don’t know the answers to these questions, because they only learn afterwards, so they guess wrongly or can’t come up with an answer, they don’t get any feedback, but still they benefit on the criteria test questions after learning.

John: What are some of the possible explanations for why pre-questioning is so effective in enhancing student learning?

Steven: So this is an active area of research, and there are many possible explanations. Just to name some of them, for example, one explanation is that pre-questioning sort of activates attentional processes that either make you aware of your gaps in knowledge and want to focus on finding the answer, or when the answer is presented, you notice it better, or simply when the instructional opportunity happens afterwards, you are more focused, and you are more primed to learn from it. So that’s a sort of an attentional class of explanations. Then there is, for instance, an error signal type of explanation, where you make a guess, and usually it is incorrect because of the lack of prior knowledge or instruction, and your awareness of that error, particularly when the correct answer is presented, there’s a discrepancy. You guessed this, and then the answer is something else. And there are potential mechanisms in the brain that are responsible for learning from those kinds of situations, and the pre-questioning triggers those mechanisms. So that’s a second possibility. There are other ones that involve, for example, your memory of making the error. Okay, I guessed this, and now this is the correct answer, and you sort of integrate this, and that becomes a more powerful learning event, because it’s not just you learn something, it’s you made a guess first, it was usually wrong, and then you have the correct answer, and that becomes a richer, more integrated representation that helps you better remember that information. So, those are three candidate explanations for how pre-questioning can improve learning. There are more beyond that, but those are some of the ones that come to mind.

John: One of the other ones that was mentioned in your paper was that it also activates prior knowledge to help prepare you to integrate new knowledge into that framework.

Steven: Yes, that’s also a potentially useful way to consider how pre-questioning may enhance learning, and this idea that it may activate that prior knowledge, so you are then better able to integrate the new information, you may be primed to learn that information, and so on.

Judith: And that explanation might even be more important in real life in class, because even when you ask students questions for a new topic, they will have some prior knowledge that they can activate, whereas in the lab we usually use texts that they know hardly anything of, so that the importance of prior knowledge is even stronger, maybe in class.

John: And however it works, it does seem to work. [LAUGHTER] So it’s an effective technique, whatever the specific mechanism.

Rebecca: And despite the evidence on the value of pre-questioning, you know that the practice is rarely used in the classroom. So why might this teaching strategy be so rarely used?

Judith: I think there can be several reasons. For one, the research field is still very young, so there was not that much time for the evidence to distribute to the public, as compared to retrieval practice, for instance, which has been far better known in the past years. And another reason may be that it may also feel weird for a teacher, for an instructor, to ask their students questions that they know they won’t be able to answer, and that might refrain some people from using pre-questioning.

Steven: Yeah, those are, I think, very good reasons for why pre-questioning may not be used that widely. I’ll add another one that’s relevant to the paper that we’re going to talk about, which is this idea that you need the materials to engage in pre-questioning, and particularly for students who know nothing or very little about the topic, which is sort of definitional requirement of pre-questioning, it would be very difficult for them to come up with questions on that topic to practice with, and also even getting them from instructors, instructors commonly, for example, in surveys, they’ll say that they’ll provide practice questions on occasion, but typically after instruction has occurred, rather than beforehand. So all of this points as sort of a materials or implementation challenge. Not only do learners and instructors not necessarily know much about pre-questioning, and it also does feel weird to use it, but even if they wanted to use it, they might not have the materials, either the time to come up with the questions or access to questions that are appropriate for pre-questioning. So there are a host of obstacles that make it difficult, at least in many circumstances, to use this particular technique.

Judith: That was the main starting point for our study, because it is not only recommended as a teaching approach, but also as a learning strategy. But for learners to use it on their own, it’s a lot harder than retrieval practice, because, as Steven said, you cannot come up with new questions before you know about the topic, you’re totally reliant on somebody else providing these questions.

Steven: Yeah, exactly. And with retrieval practice, sometimes it can be as simple as: “take out a sheet of paper and write down as much as you can remember about whatever you’re trying to learn.” You just retrieve it. You don’t even necessarily need to have a database of questions to practice, but with pre-questioning you really do need those questions, and if you don’t have those easily available, then it’s very difficult to engage in the strategy or technique.

John: Which brings us to your paper. What are the primary questions you’re investigating in the study?

Steven: Well, there are sort of a sequence, we sort of went from very simple and basic, and we got more and more specific. The first question was simply, if you use generative AI to come up with these questions, and you use them for pre-questioning, do they actually work? That was sort of the most simple question, and that was the first experiment focus. We then advanced to, okay, how do those questions generated by AI compare to questions that a relatively expert human being creates? Are they similar or different in efficacy if you use both human-generated and AI-generated questions for pre-questioning? And that was the second experiment. And then, for the remaining two experiments, we went into more fine-grained issues, specifically comparing AI-generated questions made using what we called a sophisticated or elaborate prompt that was very carefully engineered to generate the questions we wanted to see, versus a simplified or more basic prompt that any person could use without much training, so different prompts to generate these questions, and we compared that to a condition that didn’t use pre-questioning at all, but did complete an alternative activity. In this case, it was studying an AI-generated outline, so that was sort of a more rigorous test of the effects of pre-questioning to see whether using pre-questioning compares favorably to spending the same amount of time studying something else.

Judith: It’s an additional question concerned whether answering pre-questions generated by either an AI or human would generate to a different source, because if you think of a regular learning situation, you might generate as a learner your question with an AI. Well, meanwhile, the instructor might also generate the exam questions with an AI, but they might also be generated by the teachers. So, it might be worth knowing whether there is any decline in the transfer from one source to the other, and the same with the more elaborate prompt, compared to a basic prompt. If maybe a student would use a more basic prompt than a teacher.

Steven: Right, and that issue speaks to this idea of, shall we say, transfer or generalizability. So you ask someone in pre-questioning a particular question, and then you assess learning using that same exact question, that’s one way to measure learning, but as Judith just said, it could be different questions that the instructor generates, either by themselves or using an AI, or using different prompts, or whatever, and so a critical question really is, “What happens when the assessment, the questions used on that assessment differ in some way from the questions used earlier for pre-questions?”

Rebecca: So we’ve talked a little bit about the research questions. Can you talk about the design of the study itself?

Steven: So each experiment, these were online studies with online participants, adult learners, and in each experiment the learners would be assigned to either a pre-questioning condition of some type, or in some experiments a control condition where they didn’t have pre-questioning, or they studied an outline, or some other activity happened. So that happens at the beginning, they’re randomly assigned to these different conditions, and then they would engage in some learning activity, for example, pre-questioning, attempt at least two pre-questions one at a time. All participants would then, and to be clear, all of these experiments involve learning from an encyclopedic text, in this case, I think it was about brakes, the function of brakes, different types of brakes, so it’s sort of a great proxy for certain kinds of information that you would find, for example, in an engineering course, something like that. And so they engage in the initial learning activity, such as pre-questioning. Then everyone reads this passage on brakes, which contains the answers to the pre-questions, and then after that, after a short delay, then they take an assessment of learning, which is a series of questions about brakes, which include questions the same as used for pre-questioning, but also other questions that were not necessarily exactly the same. That’s the broad design. Of course, across experiments, there are variations, so to get more specific, experiment one, the one condition was pre-questioning, where they attempted AI-generated pre-questions before reading the passage and taking a test. The other condition had no pre-questioning, so they just read the passage and took the test. In experiment two, it was AI-generated pre-questioning versus human generated pre-questioning. So one group got the AI-generated questions, one group got the human generated questions. They all read the passage, then they took the test. And then experiments three and four were either what we call elaborate generated pre-questioning, the prompt that was used, or basic generated pre-questioning, again referring to the prompt that was used, or studying an AI-generated outline. So, depending on the group, they would have different activities before reading the passage, and then taking the test.

Judith: And in any of the experiments in which we contrasted different questions, both types of questions were also used on the criteria test, so that we could see differences between the tested questions and the untested questions, and in some cases even between new questions generated by the same source, either both AI-generated or both human-generated, and transferred to new questions generated by the other source.

John: A very well designed experiment, and one of the things that I appreciated about this was that it was an experiment that was pre-registered. That’s not something that happens in all cases, in all disciplines. Could you tell us a little bit about this process? It’s something I think that came up because of people attempting to replicate some past studies and not always finding that results were replicable. So, could you just give our listeners a little bit of background on pre-registration of studies?

Judith: We try to do it in a brief manner. You’re right, this originated in what we call the replication crisis in psychology and social sciences overall, in which several well-published studies did not replicate, and so there’s a counter movement right now, the open science movement, and the idea is to restore trust in science and psychological research. We want to be as transparent, as open, about what we are doing as possible, and there are several reasons for why some research findings are not that reliable. There’s a tendency for significant findings to be published more often than non-significant findings, so that we are rewarded for getting significant results, and even more for getting our predictions right. There’s a study that indicates that more than 90% of hypotheses in psychological studies turn out to be right, and that’s not because we’re so clever, that might be due to some questionable research practices. So pre-registration is one way to make sure that we write down in advance what we predict, how we conduct our study, how we analyze our data, how we decide whether a certain data point is an outlier, how we decide whether to include any covariate. So to write that down in advance, so that we’re not subject to our own biases, so that may be the one analysis that brings about the best result that we’re convinced that that was the one we wanted to do right from the start. No, we can look that up because we’ve written that down, and to make it transparent to everyone, you pre-register publicly, so you are the one who decides whether others can see it, but when you submit a paper, you can include this pre-registration, and the reviewers can see that the date of the pre-registration is really before you collected your data.

John: You mentioned that so many studies, and this is true, it’s not just in psychology, it’s also true in pretty much all social science disciplines. And two of the explanations for that are publication bias and something called p-hacking. Could you just explain a little bit of those two things for our listeners?

Judith: Yeah, the publication bias, so that indicates, as I just said briefly, that of the data that are published, most of them are significant, and that’s not realistic, because a lot of studies we conduct, they yield non-significant results, and the idea is that this is due to several biases in the publication processes. So reviewers and peer-reviewed journals are more enthusiastic for significant findings, researchers don’t have that much time to write up their findings, so they tend to focus on those that are more publishable and leave the others in their drawers. So this is also called the file drawer problem, that there are a lot of non-significant findings in the file drawers of lots of researchers in all the ivory towers, and this leads to us overestimating the size of published effects, because we don’t know about this heap of unpublished studies, and that’s a problem for evaluating how important certain effects are for us. And p-hacking is one way where one type of questionable research practice, because p is the value that indicates statistical significance, and the field has agreed that p that is lower than .05 is the threshold for importance, and this statistical significance is often mistaken for importance. And there are several ways to analyze your data that can influence whether your findings yield a p value below this threshold, and all these ways to treat your data are called p-hacking. So, you can decide to include gender as a variable that was never part of your hypothesis, but you can always see whether it makes a difference what gender the participants are, and that might bind some variance, so that your main effect is significant. You can first analyze your data, and the p-value is just above .05 and you think, well, just a few more participants, and they might be below the threshold, or you might notice that there’s this weird participant, which must be an outlier, and that often happens after your first analysis, and helps you get below this very important threshold. This often is not outright fraud, it’s just us being human beings and being subject to our biases, and that’s why the open science movement thinks research science needs the self-control mechanisms to be open with our data, with our materials, and with what we predict. And we are both committed to that, so it was important for us to pre-register the study and also to publish the data and the material.

John: Another way of expressing the issue of p-hacking is that if you torture the data enough, it eventually will confess. [LAUGHTER]

Judith: That’s nice.

Rebecca: So gruesome. [LAUGHTER]

Judith: Something else that’s related to this is also HARKing, that’s hypothesizing after the results are known, so that you don’t actually make predictions, but rather post-dictions, and then there’s this analogy to the Texas sharpshooter who draws the target after his shots, so that that is easy to hit bull’s eye, and that’s also often used to compare this research.

John: But your results are not subject to any of those issues, because you’ve done all these things to prevent that with pre-registering and sharing the data.

Rebecca: Seems like a good transition to results, huh? [LAUGHTER] Can you talk a little bit about the impact of providing the AI-generated pre-questions? I imagine that folks are really interested in this particular piece because it might save time.

Steven: So we were very excited to see that the AI-generated pre-questions were effective at enhancing learning, or producing meaningful learning, shall we say, across all the experiments. So, for example, in experiment one, just presenting two, I think, AI-generated pre-questions before reading a text passage, that was enough to enhance learning from that passage, and that enhancement extended not only to later questions that were exactly the same as the questions presented in the beginning, but also new, never before seen, questions, so there was a generalizable benefit to some extent. In the second experiment, we found that the AI-generated pre-questions yielded similar levels of learning as questions generated by competent human beings, so that suggests that you don’t necessarily need a human being to invest the time and effort to come up with the questions. An AI can potentially, an AI chatbot can come up with similar questions of comparable efficacy, and then in the third and fourth experiments, again, they were similar designs, those two experiments, both AI-generated pre-questions that were generated using a more sophisticated, detailed prompt, as well as those generated using a very simple prompt, they both enhanced learning relative to studying an AI-generated outline, so that shows the power of pre-questioning extends to not only using AI to generate the pre-questions, but even compared to using AI to generate some alternative materials that are used for some alternative activity. So those are the broad outline of the key results. Now there’s, of course, that other finding about, or other issue about what happens when the questions on the test are not the same as those that you were practiced on, or they were generated by a different source, and whether, for example, you practice with AI-generated questions, but then some of the questions on the test are generated by a human being or generated by a different AI prompt, or things like that, and I think one general way to describe those results, because they varied across experiments, was that we could still see across experiments some evidence that learning still benefits in those cases, so that it’s not essential that the questions that are used on assessment have to be exactly the same as the ones that were seen earlier. There is some degree of generalizability, although there was sort of a trend, I would say that as the questions get more and more different in source for the content targeted, the learning benefits may not be necessarily as strong, at least numerically, but there’s still evidence that the learning is not restricted to the exact same questions that you saw earlier.

Judith: And we were very happy about that, because that’s not always the case in pre-questioning studies, because often these effects are restricted to post-test questions that target the very same information, so that the transfer to these untested questions really excited us about these findings, and also the case that this very, very basic prompt, which just said, “Can you generate two questions for this passage?” worked, that really sounded feasible.

Steven: One thing I would add to that, we were really very excited by that finding, because literally that means that anyone practically could just use a prompt that is one line and come up with questions that are useful for pre-questioning or potentially other purposes. One thing I did want to note, though, is that when we were conducting the sequence of experiments that we were using ChatGPT, the version of ChatGPT was getting more and more advanced as the experiments progressed. So, by the time we got to the latter experiments and we were comparing different types of prompts, we were using a more advanced GPT version, and in that case, the questions made using, again a very elaborate prompt versus a simple prompt, they seem to be equally effective for the most part, which is again I think in part a reflection of how advanced the technology has become, and also how just generally how easy it is now to use.

John: Given that you’ve got these results, and it doesn’t require a very elaborate, detailed prompt, or even much knowledge of the subject, might you suggest that this would be a useful way of students learning things more deeply as part of their studying? Because students often use it to generate an outline, which was one of your control conditions there, and that was not as effective as having these pre-questions. Might this study suggest that this would be an effective study technique for students to use on their own when they’re learning new things?

Judith: I guess we would say yes. That was the idea, yeah. That was the hope to find evidence that it can work if they generate the questions on their own. We might also add a cautionary note to that, because we found transfer to untested questions, but that may not necessarily be the case for other questions. We noticed that there was some overlap between the questions generated, because all of them were constructed in a way that they were integrative, so they targeted more than one piece of information, and that resulted in somewhat overlapping questions, so the suggestion is a bit different compared to other studies, in which the new questions are totally different, and it might still be more helpful to get the pre-questions from your instructor, who will also post the questions for the exam, because they also tell you what this person thinks is most important, and that may be a valuable addition, but if your instructor doesn’t want to give you any pre-questions, because they don’t believe it’s effective, or because they don’t want to waste their good exam questions on students preparing for learning, that might still be good alternative to ask a generative AI to be a question generator.

Rebecca: I’m imagining that faculty hearing this are excited about the possibility of integrating pre-questions into their teaching methodologies. Do you have some advice about how they might do this, how they generate effective questions, or even like how many questions would be useful?

Steven: So, there are some tentative recommendations that we could offer, but again, these are tentative because we have barely scratched the surface of implementing pre-questions. We are still sort of exploring the efficacy and mechanism stage. Nonetheless, in terms of possible ways that the pre-questions could be generated in an effective manner, well, firstly, one thing I would note was you mentioned number of questions, so it seems at least in the studies that have been conducted thus far, the number of questions is not that many. For example, I don’t think we went more than four or so pre-questions, depending on experiments, and sometimes there’s just two. Why is that? Well, in part, because if you ask people many, many, many questions on something they know very little about, and then they have to try to find the answers afterwards, trying to keep in mind, say, I don’t know, 10, 20 questions is difficult, and so a limited number of targeted pre-questions on perhaps the topics that are most crucial is probably a good way to go in terms of number. Now, in terms of type of questions, you just mentioned the questions that we used were integrative questions, and one of the reasons why, besides what was mentioned, is that there’s some prior work on different types of pre-questions. Some pre-questions that have been investigated are so-called detail-oriented, that might just target a little fact or a number or a little detail, and then there are these integrative questions that might connect two concepts or different parts of a text passage, what have you. Those seem to be more effective at improving learning. So, in addition to the number of pre-questions, the type of pre-question probably I would recommend to instructors to think more about integrative pre-questions. Even if you want a student to learn one particular small detail, maybe couching it within an integrated pre-question might actually be better. And then there is a third sort of practical point that I just wanted to raise, which is that definitely keeping in mind that pre-questions, in a way, serve as almost like a introduction to the topic, so these are not the kind of questions used necessarily to really assess mastery in a retrieval practice sense: “Explain this concept or this theory that you’ve just learned.” If you ask such a question to someone who has not learned that concept, that theory, that question may be so difficult they don’t even know how to begin to answer such a question. Then it’s probably not the best type of question… more like a question that is in some sort of currently perhaps nebulous sweet spot of they can come up with some sort of guess, it’s probably not correct, but it’s also not so impossibly foreign that they look at this question, and think, “Well, I don’t know where to begin.” So, those are at least three tentative recommendations that I have about how pre-questions could be developed by instructors for effective use.

Judith: Something else I would like to add is that you can also first think about what do you want your students to learn the most. So, what are the most important aspects? Knowing that these pre-questions work best for what they ask for, you could make use of that and target the most important aspects with the pre-question. That’s also something related to our study, to think of the cognitive levels associated with your learning objectives, and to see that the questions are generated in a similar way, so that also guides the learners. We also looked at this in our pre-questions, and noticed that for the first experiments, the AI-generated questions were mostly on the lower cognitive levels of Bloom’s taxonomy, so mostly remember and understand, whereas the human-generated questions we used that were used in an earlier study, use comparisons, and that was not the case with our AI questions. So that was the focus of the fourth experiment, where we engineered a prompt that asked for comparison questions, and then the AI generated these questions, but only then. So that might also be something to take from this study that the Gen AI might need an additional prompt to generate this kind of question, but that might also be the case for a human being that thinks of questions that maybe we also need an additional prompt to not just ask remember and understand questions, but also target these higher levels that we name in our learning objectives, and to kind of reverse engineer our questions from what we want learners to take away from the learning material.

Steven: Yeah, and if I may add that Judith raises a very important point, which actually qualifies the finding about basic versus more elaborate prompts, even if you use a basic prompt, you know, generate questions, you can still add in there, generate questions that involve comparison. You can add some details that’s not necessarily required multiple premises in a very complex prompt, but some of those things actually might be necessary through even a process of a little trial and error to come up ultimately with questions that meet certain characteristics that are desirable for learning, including higher order type questions, questions that tap different cognitive levels, that promote the types of learning objectives you want to promote.

John: One of the things you were doing in the study is comparing AI questions with human-generated questions. Was there any apparent differences between the types of questions that humans generated and the types of questions that AI generated?

Steven: We did have sort of as an additional measure in the study… we didn’t tell them these were AI-generated questions, we asked them to guess who made these questions, and we would just give them all the questions that we had in a particular set and some were human-generated, some were AI generated. We just asked them, and they just could not tell, and so this is, I think, part of a larger challenge now, which is differentiating between AI-generated and non-AI-generated materials. Now, not only are the questions as effective as those generated by human beings, but also human beings cannot tell that they were AI generated, but I thought that was kind of a fun little thing, and it speaks to this larger challenge that we have, whether it’s trying to detect AI use in our students’ essays, other things like that.

Rebecca: We always wrap up by asking, what’s next?

Steven: Well, in my research group, and also in collaboration with Judith, we’re really interested in exploring a variety of aspects of pre-questioning, in terms of, for example, ways to implement it in authentic educational circumstances more successfully. So, for example, studies in the classroom, or studies exploring how you might integrate pre-questions into learning activities that are very common, like reading a textbook chapter, things like that. So we’ve explored AI-generated pre-questions, we’re very interested in implementation that could be easily translated to real-world practice, so that is one area of very active interest related to pre-questioning.

Judith: And then a related question in our labs… Well, I thought of it because it’s somewhat related to the current idea, because I’m currently working on a study with a PhD student where we want to investigate whether spacing, distributed practice, which is recommended both as a teaching strategy, but also as a learning strategy, but it’s investigated almost always so that the entire schedule, sticking to the schedule, that’s decided by the experimenter, and our idea was we really don’t know whether that works as a learning strategy, so we implemented it in a way that gives some responsibility for the learning, for sticking to the schedule, to the learner, and see whether it’s still an effective strategy, and also to see whether something like gamification might help learners become better spacers.

John: You’re doing some great work here, and actually that reminds me a little bit of the last interview with Steven and his co-author on another study that looked at classroom implementations of interleaved practice, because a lot of the interleave practice studies had been done in the lab and it was nice to see what happens when it was actually introduced in the classroom environment. You’re both doing some really good research, and we’re curious to see what you’re going to find in those new studies. Thank you.

Judith: Thank you.

Steven: Thank you.

Rebecca: Yeah, thank you so much for joining us.

Judith: Yeah, thanks for the great conversation.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

[MUSIC]

448. AI-Aware Teaching

While there is substantial debate over the appropriate role of generative AI in higher education, one area of agreement is that AI cannot be ignored. In this episode, Annette Vee, Marc Watkins, and Derek Bruff join us to discuss what faculty need to know to be AI-aware in their teaching.

Annette is an Associate Professor of English at the University of Pittsburgh where she has been actively involved with AI initiatives. She is the author of Coding Literacy: How Computer Programming is Changing Writing and is co-editor of TextGenEd: Teaching with Text Generation Technologies. Marc is a Lecturer in Writing and Rhetoric and an Assistant Director of Academic Innovation at the University of Mississippi, where he directs the AI Institute for Teachers. Derek Bruff is an Associate Director at the University of Virginia’s Center for Teaching Excellence, where he supports faculty in integrating generative AI in their teaching. He is the author of Intentional Tech: Principles to Guide the Use of Educational Technology in College Teaching and Teaching with Classroom Response Systems: Creating Active Learning Environments. He is the host and producer of the Intentional Teaching podcast. Annette, Marc, and Derek frequently serve as keynote speakers at academic conferences and write frequently about AI and higher education on their blogs. Annette, Marc, and Derek are the co-authors of The Norton Guide to AI-Aware Teaching.

Show Notes

Transcript

John: While there is substantial debate over the appropriate role of generative AI in higher education, one area of agreement is that AI cannot be ignored. In this episode, we discuss what faculty need to know to be AI-aware in their teaching.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guests today are Annette Vee, Marc Watkins, and Derek Bruff. Annette is an Associate Professor of English at the University of Pittsburgh where she has been actively involved with AI initiatives. She is the author of Coding Literacy: How Computer Programming is Changing Writing and is co-editor of TextGenEd: Teaching with Text Generation Technologies. Marc is a Lecturer in Writing and Rhetoric and an Assistant Director of Academic Innovation at the University of Mississippi, where he directs the AI Institute for Teachers. Derek Bruff is an Associate Director at the University of Virginia’s Center for Teaching Excellence, where he supports faculty in integrating generative AI in their teaching. He is the author of Intentional Tech: Principles to Guide the Use of Educational Technology in College Teaching and Teaching with Classroom Response Systems: Creating Active Learning Environments. He is the host and producer of the Intentional Teaching podcast. Annette, Marc, and Derek frequently serve as keynote speakers at academic conferences and write frequently about AI and higher education on their blogs (which we’ll link to in the show notes). Our guests are the co-authors of The Norton Guide to AI-Aware Teaching. Welcome, Annette, and welcome back, Marc and Derek.

Annette: Thank you.

Derek: Thanks for having us.

Marc: Yeah, thank you, guys.

John: Thank you for joining us. It’s great to talk to all of you today. Today’s teas are:… Annette, what is your tea? [LAUGHTER]

Annette: I have to confess that we are out of tea season. For me, it is warm, and I’m on my third floor office, where it is not air conditioned, so I’m just drinking water. But I like tea. I like herbal tea… when it’s cold outside.

John: And Marc, are you drinking tea?

Marc: I wish I was. Our tea kettle in the office is broken today, so I have a very anti-tea Celsius that is a Kiwi Guava flavor, so it has caffeine and its liquid, [LAUGHTER] that’s the sort of the most related thing you can have with tea. But yeah, unfortunately, I’m like Annette, I don’t have access to good tea right now.

John: And Derek?

Derek: Well, I’m 0 for three. I am drinking coffee, [LAUGHTER] but I am drinking it out of my tea for teaching coffee mug.[LAUGHTER]

Rebecca: I think that might be blasphemous. [LAUGHTER]

Derek: I think I should get some points for having the on-brain coffee mug today.

John: Yes.

Rebecca: I think it’s good. It’s good.

Derek: Thank you, John, for gifting me a coffee mug when I saw you a couple of years ago.

Rebecca: It’s a tea mug. [LAUGHTER]

Derek: Not right now.

John: it’s a tea for teaching mug, and I think it was a coffee mug last time too.[LAUGHTER]

Rebecca: Yeah.

Annette: Get into some Wickenstinian conversations about definitions here. [LAUGHTER]

John: Well, I went to the POD conference with, I think, 48 tea for teaching mugs, and I think I came back with about two or three of them, because the whole place was filled with former guests from the podcast.

Marc: That’s great.

Annette: That’s lovely.

John: It was nice to be able to distribute them. And Rebecca, your tea?

Rebecca: I have British breakfast, nice and piping hot.

John: I have darjeeling tea today, which I picked up at the tea caddy at Epcot at the OLC conference last fall.

Rebecca: So we’ve invited me here today to discuss the Norton guide to AI-aware teaching, apparently, and not tea. [LAUGHTER]

Annette: It’s good because the conversation’s over otherwise. [LAUGHTER]

Rebecca: So, can you tell us a little bit about how this project came about?

Annette: Sure, I can start with that. So, this project was kind of a brainchild of Norton themselves, and I had been working with Erica Wnek, who is one of their fabulous editors there, and who works with composition, in particular. I’d been doing the Everyone Is an Author book. They wanted to add a chapter on AI for their next edition, which just came out, I think, in the fall, and from there we started talking about what else Norton might be able to do to support teachers with AI, and I started writing for a Substack, a newsletter that they launched, and worked with Erica primarily on that. It was the first time that Norton had done a Substack like that, and it ended up being reasonably popular, and then there seemed to be some demand for further guidance about AI teaching, but in particular teaching in the midst of AI that wasn’t just, “Hey, let’s lean into AI or here’s how we kind of route around AI,” but something a little bit more measured that was kind of the voice that I had been using in that blog, and so Erica asked me, “What would it look like if we did some sort of guide like that?” At first, I thought, “Well, there’s so many guides out there,” but ultimately I was interested, and even better was that they were really interested in recruiting some excellent co-authors, and so from there they brought on Marc and Derek, and so I can pass that off to them, which I think has really been wonderful for the project.

Marc: It has indeed. I really have learned a lot from both of you writing this, and it was kind of a sprint to go through this, and I think as we were writing it, we were both going through our own sort of basic just feelings about AI and seeing how other people were dealing with it too. We’ve been there for the. Last three and a half, four years, and asking ourselves some very deep questions about our own work with this technology, too, where this is coming to intersect, and trying to help faculty tred a path forward.

Derek: I’ll add that, as Rebecca mentioned, I’ve written a couple of other books on teaching with technology, and I think I have a writing voice that works well with Annette and with Marc, and with the goal of this project, a mix of what does the research tell us, to the extent that it tells us anything, what are the big ideas we need to be grappling with, and what does this look like in practice? I think that’s one of the things I really like about this book, is there’s lots of examples of how faculty are adapting and responding to AI in very concrete ways. That’s what I love to do, is to talk to faculty about what other faculty are doing, and help them see how they can adapt that to their own teaching. Yeah, so happy to be a part. And also, I would say I haven’t co-written before this project.

Rebecca: I didn’t know that, Derek. You reveal that now. [LAUGHTER]

Derek: I was a little skeptical. I kind of like to be in my own space when I’m writing, but this has been a really, really enjoyable process of drafting and getting feedback and revising and commenting and figuring out the structure together. I like the co-writing a lot more than I expected I would.

Annette: That’s really nice to hear. I want to say, too, that Norton brought us together, which I think is wonderful, because they just had like great vision of how we could each bring different expertise, which I think was like absolutely true. But the other thing is that I knew Derek and Marc’s work before this, and so I was really, really glad to be able to collaborate with them, and I do think, like BTS or other bands that the studio brings together, sometimes there’s just synergy, and it works. [LAUGHTER]

John: I really do like the title of AI-aware teaching, which I think fits nicely with Annette’s comment that it was somewhat in the middle, not at one of the poles, as many of the other books and approaches tend to be. Before we start talking about AI and teaching with AI, could you explain what you meant by AI-aware teaching?

Annette: So, I can start with that one. As Marc said, we were all in the process of writing this book, confronting our own feelings about AI in complex ways, and I think all of us had been wrestling with this, of course, before the book, because we’d all been working in this space for years, but the fact that I am absolutely an AI user, I find it really interesting. My work is in writing technologies, it’s the most exciting writing technology, certainly in my lifetime. So, there’s all these really interesting feelings, and then as a teacher, there’s these other feelings about students and shortcutting and the future of higher ed, and all these things, but the main thing is, as a technology, I think it’s just really important to be aware of what it is and what’s out there, and what that means is, for AI-aware teaching, it means understanding what the technology is and does, and not, you know, what it did three years ago, but what it’s doing now, and what are the possible future trajectories for it, and that includes all sorts of things about environmental impacts and politics, as well as the technology, and then also what students are actually doing with it, not what we would like them to be doing with it, or what they say they’re doing with it, but what they’re actually doing with it, so that involves a little bit of research and listening, and then figuring out what it is that we want students to get out of our classes, rethinking our learning goals, things like that, and then kind of recalibrating, given the current landscape, how we get to those learning goals that we have had, or revisiting those learning goals, maybe they need to be revised, and that is, I guess, my kind of sense of AI-aware teaching.

Derek: And I’ll add that I feel like there is a lot of discourse in higher ed that seems to indicate there are two camps: either you are refusing to use AI or resisting it, or you’re all in and you’re doing all these things with it, and I like the AI-aware framing, which is to say those could both be reasonable choices, but we need to understand what we’re dealing with, both the technologies and our students and our own teaching goals, and then make informed intentional choices. And I would argue that most faculty, once they’ve gone through that process, land on some mix of resisting AI in some parts of their teaching and engaging with AI in other parts of their teaching, and I think that’s okay, and I think we have a lot to offer in the book for faculty who are any mix of those two poles in how they actually practice AI-aware teaching.

Marc: Absolutely. I find myself sometimes in the middle of an assignment, designing assignment, being drawn into, let’s use AI, this aspect, let’s absolutely resist, refuse using AI in these different aspects, and then thinking about, from the student experiences, how they’re going to go through those same types of things to confront it. I think Annette’s point too is very apt for a moment too. The interface behind ChatGPT has not changed, but the models have underneath it, and the expectations I have from a lot of faculty is that they’ve tested these systems back in 2022/3, or 24 that they have a singular experience using them, and it was not very good or productive. Now things have changed pretty radically. These are not just language models, they’re, in theory, world models gainng the simulations of these different types of experiences, and so we have to be aware of what that is going to do for ourselves and for our students, and there are some, I think, very good benefits that can come out of this, but also be very cautious about it too, just trying to find that balanced pathway.

Rebecca: Can you talk a little bit about the structure of the book? There’s a couple parts to it. Can you reveal the underlying structure?

Derek: Yeah, I’ll take a stab at that. The book has two parts, and like these books, you can read any chapter in any order you want. I never do that. That’s not who I am. I read things from front to back. [LAUGHTER]

Annette: You’re revealing a lot of things today about yourself, Derek. [LAUGHTER]

Derek: People tell me they read books in other ways. [LAUGHTER]

Rebecca: All these exclusives…

Derek: But, I do think part one is meant to be moved through somewhat linearly, and that it walks you through the AI-aware teaching idea, so that we’ve have a chapter on what AI is and what it does. We have a chapter on how students are using AI, as best we can tell from the research and the surveys and the focus groups that have been done. And so the goal of part one is to help you do those aspects of AI-aware teaching, and then hopefully land on an approach to AI that really fits your course and your goals, and to start to shape how you might communicate that approach to your students through policies or guidance. Part two is very much more a random access part. We have a chapter on in-class activities that are AI aware and out of class activities that are AI aware. We have a chapter on AI and reading, a chapter on AI and writing, a chapter on AI for tutoring, and so it’s much more about where do you see AI showing up in the particular pieces of your courses and your teaching, and what ideas can you get to help you make again more informed intentional choices in those particular aspects. We also have a chapter on AI and academic integrity, because that’s kind of the elephant in the room. [LAUGHTER]

Annette: …that’s the first thing everybody asks us at workshops.

Derek: So we put it last.

Marc: It is, yeah. yeah, yeah. You got to get through the book if you want to get to the last chapter.

Derek: Well, I do at least.

Annette: Yeah, yeah.

Rebecca: I like how it was almost an afterthought there, In the way that you’re presenting it, [LAUGHTER]like it really was an elephant in the room.

Derek: Yeah, because I feel like it’s where so many conversations start, but I don’t know that those conversations are always super productive. I think it’s much more productive to get more grounding in who our students are, what the tools are doing, what our goals are, and then once we’ve got some of that infrastructure, that kind of mental infrastructure in place, we’re in a much better position to make good choices about how we respond to academic integrity concerns.

Marc: Imagine that, approaching this from not an emotional standpoint, but one that’s actually based on evidence of those types of things. I kid, of course. I mean, look, a lot of people are at that really gut-wrenching situation three, almost four, years since ChatGPT was launched. I do think one of the goals that I had personally writing this book, too, was to figure out ways to help faculty move past that initial gut reaction of just thinking about ChatGPT, generative AIs, as a cheating tool, and to start thinking about more broadly about when is it acceptable to use this, when it is not. How can we articulate that to our students through our learning outcomes? And what we did, I think, a really good job in the book, too, is looking out at the world of academia, too, and finding wonderful examples from faculty in how they’re actually incorporating a lot of these different types of experiences for their students, and how they’re sort of wrestling with these questions too.

Annette: Yeah, absolutely. I do think that that’s really the most exciting part about this book, is that we feature so many really concrete, tested examples from teachers at community colleges, at research universities, teaching colleges, just like everywhere, and for a long time I ran our graduate training in the English department for teachers, and now I run faculty development for AI, and this book sort of answers a lot of questions of sources that I would like when I’m teaching those kinds of things, like what do I want to assign that gives a kind of theoretical background, and also that kind of trust students, acknowledges student agency, trusts teachers and their expertise in whatever disciplines they’re teaching in, because it is meant to be something that moves across disciplines, and then gives them a whole bunch of examples, and there’s not answers in there, but there’s a whole lot of things to adapt, and so hopefully that’s useful for working teachers.

John: As you’ve noted, one of the concerns that many faculty have is that students may use it in ways that are not conducive or supportive of their learning. And actually, Derek had a recent podcast where he was talking with some people in math, how earlier on it was easy to detect the use of AI, because AI tools often got things wrong, but that’s much less true today, especially for the paid versions and the deep thinking versions of these tools. So, what are some strategies that faculty can use to assess student learning and their skill development in a world where they have access to these increasingly capable AI tools?

[AWKWARD PAUSE]

Marc: Let’s keep the awkward pause in here… [LAUGHTER]

Annette: I do feel that awkward pause. [LAUGHTER]

Marc: I do. Well, I can take the lead here on this one, if you guys don’t mind, then I’ll hand it off. I think strategies are evolving. I think that how we are seeing these tools being deployed and marketed to students, too, is part of our awareness to see this sort of version of it too. AI detection is also changing alongside all these other tools as well. I’ve really leaned in personally for myself, and also included examples too, about emphasizing disclosure when AI is being used, both from the student perspectives, but I think it’s also important for faculty to start having these conversations themselves, too. We are not only seeing students use AI, we’re starting to see faculty now really adopt these in all parts of the course, including in some cases grading. So, if you’re going to use AI in that sort of manner, too, you probably want to think about disclosing it as just sort of a baseline expectation and giving some great examples about what, as I said, in a recent talk, too, there’s performative disclosure, there’s exhaustive disclosure, we give them everything, but then there’s authentic disclosure, where you want to have a conversation, we want to have a dialog about how AI is being used, because in some ways you’re genuinely curious about how this is actually being used by students, and if it can be able to support them, and there’s lots of analytics you can use with the course management system, too, to track student behavior. That’s one thing to kind of be there to just make it visible, but the main strategy there is that you really want to have a situation where you can have conversations with them in some way, shape, or form. That’s very difficult for a high enrollment class, or if you’re teaching six sections, which some of our faculty are doing, and can’t actually have those types of relationships,

Derek: I’ll add that what I hear fairly consistently from students through surveys and panels that I’ve been a part of is that they want some clarity on what the boundary lines are around what’s appropriate use of AI and what’s not, and there’s lots of different ways to try to communicate those boundary lines. There’s the AI assessment scale, which says for this assessment, these are the types of uses that are okay. You can do it at a course level and say it’s okay to use AI for figuring out a hard concept or brainstorming ideas, but you can’t use it for drafting, or you can use it for editing. We’ve seen lots of different examples of how faculty are helping to define those boundary lines for students. In some cases, to Marc’s point about good conversations, they are figuring out those boundary lines with their students through conversation. They’re kind of coming together to come up with good policies around this kind of thing. And I think, though, an important piece of this is that most students want to know kind of what’s allowed and what’s not allowed, right? That’s a big piece of it. But I think students are also concerned about what’s going to help my learning and what’s not going to help my learning, and they are not always in a good position to know what uses of AI are going to be conducive to their best learning. They’re novices, they’re figuring out how to learn. We may have a better idea of that. We may be figuring it out too, in many cases, because AI is so new, but it’s not just about how can we secure our assessments. And from the student’s perspective, it’s not just about how can I stay out of trouble, but there’s a strong element of how can we actually help learning happen in the ways that are meaningful, and figuring out those boundary lines together is one way to get at that shared goal that we and our students both have.

Annette: I think that’s important, that shared goal, because one of the things that we know from what students tell us about their experiences of AI in classes is that they are really frustrated about a lot of what’s happening in that there is lack of clarity in policies, most of what they see in policies is do not do this, rather than here’s some good ways to use AI, and they find that sometimes infantilizing, I think, and unrealistic, and then they also don’t like it when their peers are overusing AI, and that kind of cheapens their own experience and degree and their effort, and so they have a lot of feelings about it. Marc started out by saying, talking to students, I think that’s really the crucial thing, and I think there’s ways you can do that and certainly we talk about that in the book, you can do that in large lecture classes and through asynchronous surveys, all sorts of ways. It’s possible to hear from students in a lot of different ways, but I think that’s super important, and also one of the reasons why I said in the answer to the last question about student agency is that we need to make sure that students are getting what their goals are out of our classes, we have external goals and things that we want them to get out of classes, but they have their own goals, and they have their own interests in learning, as Derek mentioned. So, can we help them align those goals that they have to what we’re doing in the class, and help them articulate how this class is going to help them in the future, because our students are also nervous about job prospects and all sorts of things, and I think the more that we can lean into the fact that we’re there to support their learning, we can help all of those conceptual things. The other thing that we talk about in the book some is the University of Sydney two-lane assessment, which I think is a helpful framework, thinking about, on the one hand, secured assessment, and that is like proctored exams or things where you have a really clear idea that the person who is represented here in this space is the same person you’re assessing learning of this particular person, and then you’ve got a lot of spaces of open assessment where students are doing homework, low key, et cetera, they may or may not be able to use AI. And so, I think that one of the reasons why we get that question all the time, about like, how do you stop students from cheating, or whatever, in workshops, is that the labor that teachers are going through is so significant right now, and nobody wants to do the surveillance and policing, zero teachers want to do that, and trying to figure out how do you actually assess what a student is doing is so much work, and so if we can narrow that space down, and then let students play around a little bit more with here’s possible positive ways of using AI to enhance your learning, in ways that they may not necessarily know already. As Derek said, they want to work on their learning, but it’s not always clear what that looks like, and that’s part of why we have teaching experts in the room, that is us, right? So we’re giving all very complicated answers to this, and that’s because there is no silver bullet. We keep coming back to that in the book. There are no easy answers, and we do not give easy answers in this book, because they’re just aren’t present.

Marc: That’s absolutely right. And she also hit on something that I think we were all really attuned to, is the labor of this going through the sort of response to AI. Doesn’t matter if you want to engage AI to or refuse it to, you’re still going to have to really focus on work, and part of what we’ve been doing too is trying to find different strategies to support faculty, so that’s not so overwhelming, trying to think about even taking some steps from, like, Jim Lang’s small teaching moments to bring up these types of conversations, because we realize that not all faculty are going to have support from wonderful teaching and learning centers. Some faculty are going to be contingent and sometimes not have much support at all, too, and they still are going to want to practice some type of AI awareness with their class and their actual day to day lives, so labor, I think, is a really big one we’ve been talking about as we go through it.

Rebecca: I think one of the spaces we get a lot of questions is on the asynchronous online environment, in particular, because it’s extra challenging to figure out ways to do assessments or to do quick checks for learning in that environment where it’s completely vulnerable to AI in the ways that maybe we have historically done these things. Those that teach in in- class settings have moved to using blue books and some other things occasionally. What advice do you have for some faculty that are specifically in this asynchronous environment that are hitting some slightly different challenges than you might encounter in a face-to-face class.

Marc: I think the best piece of advice I could offer, because I drafted, I think, a chapter that we try to talk about this, is to breathe and not take this personally. It’s not against you, and there’s certain things you can control. Sometimes you have options with your modality you pick as a teacher, sometimes that’s picked for you, but you can also really search for ways that you are more present inside of your online class that makes it more embodied for yourself and for your students. I’m doing that myself now too, with my hybrid and online classes too, trying to be more there with them, having more conversations, and just also making myself more available to them too, because one thing that I think we’ve all noticed is that if you are doing an asynchronous course on autopilot, not only is that very prone to AI misuse, but also there’s a debate too, if you’re not there as the teaching expert, that you’re not there as the person in the room, too, what is necessarily being lost if a student is going to use AI in that context? So that does really reveal a lot of our practice that we’ve gone through to start thinking about it. For the security aspect, too, there are layers of things that you can do in these courses. You can, if you’d like to engage a lockdown browser for some assessment types, secure it. That’s not going to work for everything, but you can also switch over and think about doing a mix of video assignments. I do a lot of work instead of the discussion board in my online classes with videos. I don’t do the talking head thing anymore, too, where they just read a script, which, because that script is generally generated by AI, I have them actually write something or do something else there with, if they’re doing a multimodal project, share their screen and work in real time on the screen too, or they do a recording of that asynchronously, so I want them actually working in practice, showing me that process, because even the AI is involved in that too. They’re still working through those decisions, they’re still thinking about those things. So, there are some strategies we can use. They are going to be complicated. I do think we need a little bit more support from our course management systems to work with us, especially now that agentic AI is here. How many students know about agentic AI, I think is a very good question. I think most of them will associate generative AI with ChatGPT, just like Google is synonymous with search. I don’t think they’ve risen to the point, too, of they’re all going out to use agents to go through and crash their courses. I would actually say that faculty are maybe more aware of that than students are, which is a good thing to start thinking about where this is headed, but it is something to be thoughtful about as we go forward too, because as these systems become more and more advanced and also more autonomous in what they can do with the different workflows, it’s not just gonna be students that are going to be adopting them, we have news that Canvas is going to have an AI teaching agent there as well, with certain limitations and guardrails, but this is just sort of the start of this new version of working with these tools, and we need to start thinking about what sort of boundaries we want to put in place, both for students and for ourselves with them.

Derek: I think for the online space, as you say, Rebecca, the online asynchronous is kind of the perfect storm of AI problems. I’ve been thinking that there’s an impulse to try to solve that problem through better course design. If we can have more transparent learning objectives, if our assignments are more authentic, if we can combine those, so students know here’s why I’m doing this project, and here’s what I’m going to get out of it, and it aligns with my personal and professional goals. Those are all things that are going to help students resist an unhelpful use of AI and lean into the hard work of learning. I don’t think that’s sufficient. [LAUGHTER] I do think that’s necessary. I think that’s good teaching to begin with. I do think we also need to have a cybersecurity mindset that says there’s going to be bad actors who are using tools that are problematic, and we’re going to need help from our IT infrastructure to figure out how we can block things and prevent things. I think there’s sometimes an impulse just to do that, right? Like somehow we can technology our way out of this problem. I really do think we need both. I think we need better curricula that better align with students’ goals in more transparent ways, and we also need to think about the technology tools that we’re going to use. So, one of the metaphors I think I shared with our author group was the notion of a locked door, so I have a locked door in the front of my house. It’s my understanding I haven’t tried this, it’s my understanding that with about an hour and a few simple tools and YouTube, any one of us could figure out how to pick the lock on the front of my door. The technology itself is not super secure, but it communicates to anyone coming by, you’re not supposed to just walk in my house, you need to ask first, and so sometimes it’s tempting to think about the kind of anti-cheating technologies as like a secure barrier, like we’re going to keep out all of the cheating, we’re not going to keep out all the cheating, we’re going to keep out some of the cheating, but we’re also going to communicate what those boundary lines to students are a lot more clearly to say these are signals we give to students to say don’t do this and please do this instead. The research on academic integrity says most students don’t cheat because they’re evil people, [LAUGHTER] mostly it’s because of the current environmental situation that they’re facing. And so if we can create those signals and boundary lines that help students make a better choice in the moment, I think that’s a lot of the good that we can do in this case.

Annette: I love that metaphor of Derek’s. I think it’s really helpful, but one of the things that signals is not only to the students who would be cheaters, who might, if it were just a little bit easier, make a different decision, but it’s also indicating to students that their fellow students are getting the same message, and so there is a “here in this space we respect academic integrity, and that means you should also feel comfortable doing the real work and leaning into the hard work of learning,” as Derek said, because they do feel like suckers if they’re doing that hard work and their peers are not, even if it’s not on a curve, you know, whatever, there’s like that deep inherent feeling of unfairness, so I think that’s really crucial. Marc brought up the point of agentic AI, and how that might be more of an issue in online teaching, and that maybe faculty are more aware of it now than students, which is maybe a good space for us to be thinking about that. One of the reasons why I feel like I’m not as worried about agentic AI is that it is so clearly an academic integrity violation. Something else is impersonating you and doing the work of you on a platform, which isn’t to say that it won’t happen, of course it’s happening even now, but that casual, like, is this or is this not against the policy? It’s really against the policy you survey students, and they’re like, “No, flat out, that is just absolutely wrong,” versus, “well, this policy says no AI, but it doesn’t go into details, and I did some brainstorming, but I didn’t use it for writing,” like that is “who knows, like maybe,” they weren’t really specific. So I think that’s more the space that when we talk to students, we can help them think through, “Okay, this is why I really don’t want you to use AI for brainstorming… for these specific reasons here, and maybe it’s good in other contexts, but not here.” And so this is why I think agentic AI, certainly we should be paying attention to it, but I just do not envision a future in which all college students are absolutely botting their way through their degrees.

John: You talk about a number of strategies that can be used in terms of getting student buy-in to reduce the excessive reliance on AI, but are there any other strategies that you can think of that might be able to deter students from using AI in ways that are harmful?

Derek: So, I can think of a couple more. We’ve talked about the big ones around having transparency with students about learning objectives and what they’re going to gain out of this learning experience, we’ve talked about more authentic assignments that kind of connect more with students’ interests. I think a lot of faculty are experimenting with alternative grading schemes that are changing the motivational landscape for students and giving them more opportunities to revise and resubmit and try again. That’s going to take some of the high stakes away that might lead students to take shortcuts. Again, not a silver bullet, but it’s part of those environmental factors. We talked about online courses, and it’s easy sometimes to forget that even in online courses our students are humans with bodies in real physical environments, and so I’m seeing some faculty start to think creatively, like Marc was saying, about how could we do some process tracking where they are doing some work, and it’s recorded, or are there assignments that are going to get them out of their laptop zone and into the real world to do something that’s connected to your course? Again, not a silver bullet, but adding that to our mix of engagements, I think, can help students shy away from shortcuts. And the other thing I would say is, I referred to this earlier, students are learning how to learn when we encounter them. We are probably better learners than they are, maybe not in all cases, but we’re experienced academics, we’re pretty good at learning. AI is changing how we approach learning in our own disciplines, but what I would argue for is giving students more opportunities to do some metacognitive work, reflecting on their own learning in our courses, and thinking about how they used AI. Was it helpful? Was it not helpful? They had to read a hard chapter this week. What were the strategies they used to make it through that hard chapter? Which strategies were more or less helpful for that? And some of those strategies may involve AI, and some may not. Our students are going to go, and they’re going to have access to AI tools when they leave us. Hopefully, we can equip them to use those tools with some level of discernment, and that involves some self knowledge and some metacognitive thinking, and so I think we should start to build those opportunities in our courses for students.

Rebecca: Well, you’ve teed this up nicely, Derek. Thinking about the workplace and how AI is used in the workplace, are there ways that we should be rethinking or adjusting our learning objectives for our courses to either cycle out some skills or particular things that maybe were in there before, and put in some new things. I think at the top, Annette, you talked a little bit about a moment to reflect on [LAUGHTER] learning objectives and potentially take it as an opportunity to revise.

Annette: Yeah, absolutely. Derek used the word discernment, and that’s a word we really hooked into in the book, is thinking about how students can exercise careful judgment about when to use AI, when not to use AI. We all know a dozen examples of contexts in which people have used AI in places that are absolutely inappropriate, either for human means, either for signing off on something that shouldn’t have been signed off on, hallucinations, inaccuracies, all sorts of things, and I feel like my goal is to have students [LAUGHTER] not do that, like don’t put us in the headlines, you know,[LAUGHTER] which I think is something that goes over well with administrators at any university. But, really it’s like, how do you make good judgment on what AI looks like when you go into the workplace, and there are so many workplaces that really are leaning in, and it is kind of unregulated in a lot of ways. It’s surreptitious, it’s tacit, it’s the secret cyborg that Ethan Mollick talks about, and so I think it is really important for students to not just like, “here’s how you do prompt engineering,” but something that’s much more sophisticated about how are you supplementing the knowledge that you have when you’re using these tools.

Marc: Yeah, the goal with discernment, too. I mean, I’ll just give you an example, too. I just spoke with our law school, and they are getting a lot of interesting signals from the legal industry about how they want AI to be used and not used. You can actually go on to the AI hallucination case database. Someone’s actually set this up. There’s 1300 hallucinated cases, along with the sanction that the judge, who, of course, was very upset, found out AI was used in a way that created fictitious case law. So there is that issue too, of using AI inappropriate.

Derek: And just to clarify, these are real lawyers who have submitted briefs to courts where there were hallucinated content in those briefs, and they were caught on it and called out and punishment. And there’s a website that tracks all this, and I think it’s just a weird, strange world we’re living in, where that continues to happen, and someone is tracking all of that.

Marc: Indeed, yeah. And to make it even stranger now, the opposite end of the spectrum is they’re hearing from a lot of corporate interests too, is that when they go to bill them for their hours, they want to have AI listed there, not as disclosure for hallucinations, but how much money did you save us by using AI, or did you use enough AI. So there are some really wild mixed signals, depending on what your discipline is, about what your industry is, as well, too. And helping students navigate that is a big challenge. Helping faculty now navigate those questions, too. It’s like, okay, do I need my students to learn about AI to avoid hallucinations? Oh, wait, you have to learn about AI because you’re being billed for it if you don’t use it enough. So, it’s really fascinating just to see this play out. That’s just one example. We have this in different industries too. I’ve talked with folks in hospitality management. AI is a massive tool in that industry right now, and they’re having some issues that they’re doing this with too much AI for the audience and upsetting them too, but also, having that same sort of situation too, where your company you’re working with, or you’re supposed to be working with, is demanding you use AI to cut overhead. So it is going to be a challenge to navigate, for sure.

Rebecca: So, are we revising our learning objectives?

Derek: I think, to Marc’s point, I think it depends a lot on the discipline that you’re in and how things are shaking out. So, like in computer science, the goal of learning how to write basic code is less important now. The goal of figuring out how to evaluate the quality of some other code that you’ve been given, whether it was written by a human or an AI or not, the kind of reading and evaluating code that’s more important now. Over in a writing class, though, writing the first draft is kind of the equivalent to drafting that code, right? Is writing the first draft not important because AI can do it? I don’t think so. I think the ideation and brainstorming that happens during that first draft is super important, and so similar moves with AI in different disciplines may be thought of very differently in those disciplines because of kind of what’s under the surface there, and so that’s why I think a lot of these conversations, around learning objectives in particular, need to happen within the disciplines and within the fields, particularly if those disciplines are having some conversations with how the workforce is changing.

Annette: One of the things that we talk about in the writing chapter, to kind of extend a little bit what Derek was talking about, is that there are different important aspects of the intellectual process across different aspects of writing, so I teach in an English department, and that means that that ideation, the brainstorming, the writing is kind of the argument, and that’s the work. I also spent eight years working in a neuroscience lab, and in that lab I did some of the writing. That was inconsequential, because, like, I didn’t design the experiments, I didn’t, I mean, sometimes I actually did sleep deprive [LAUGHTER] the rats, but I didn’t really do the work, the intellectual work of the lab, and so I didn’t get an authorship credit, rightly so. And so when we look across disciplines, it is really important. This is why we keep saying in the book, we trust you to figure this out, is like you have to think about like what is the intellectual work that’s happening in your class and in your discipline and for your students, so that you can pinpoint that intellectual work and make sure that it’s enhanced or at least not undermined by AI. And then maybe, such as what Marc is talking about with the legal profession, you need to integrate AI or help students think about how they might thoughtfully do that, because they’ll need to have that skill moving forward,

John: And by connecting to what people in the fields where students are likely to go into when they graduate, it can create more authentic learning opportunities where students are using AI in the same way they might be expecting to use it later, I think. We’ve talked a little bit about all the concerns that faculty have, but there’s lots of ways in which AI might be useful to assist with student learning. Can you talk a little bit about some ways in which AI tools can be helpful in increasing student learning?

Derek: So, I’ll jump in. I’ve had a journey on this, because I feel like when I started writing with these two, I was the like all in on AI, and let’s use it to augment our teaching and then create new learning experiences for students. The more I went through the writing process, the more I’ve talked with faculty this past year, the more mixed I am, and the more kind of avoiding AI piece has been a much more important to think about, like we do want to protect the space for students to learn in meaningful ways. That said, here’s my current metaphor for thinking about this. [LAUGHTER] You’ve probably heard of the forklift in the gym metaphor. If you want to move weights around in the gym, you don’t bring a forklift in and move them, you go in and you lift your weights. That’s the point of the exercise, is for you to do it and to let your muscles get that strength training. Yes, the forklift will move the weights, but that’s not the point. Some uses of AI are like that, they are doing the work for students, they are moving the weights for students, and the students aren’t getting anything out of that. But I would argue that there’s another machine that actually you do find in gyms, which is the treadmill. I’m a runner, when I want to run and it’s 35 degrees outside and raining, I don’t want to run outside, I want to go to the gym and run on a treadmill. The treadmill is not doing the work for me, but it is creating conditions under which I can do the work, and so those are the uses of AI that I was initially drawn to, and I continue to be drawn to. How can we use AI in ways that are going to create new learning opportunities for students without robbing them of the exercise themselves? And so that’s things like having students critique the output of some AI chatbot that’s doing some task associated with your course. It gives them a chance to practice the knowledge and skills they’re building. This can be a tricky assignment if the AI does too good of a job in whatever you’ve asked it to do. There may not be enough to critique, but lots of faculty are finding places or particular AI tools that still have kind of big flaws in them. We have one example in our book of a math faculty member who had an AI generate flawed proofs to mathematical statements, and the student’s job was to evaluate those proofs. That’s a great opportunity for practice. Other faculty are having AI give feedback to the students on their work, and I think you have to do that in a thoughtful way, so that students aren’t just kind of taking the AI feedback. As gospel, that they are in a position where they can question that, maybe compare that to feedback they’re getting from their peers on a particular assignment. You also don’t want to kind of abdicate your own role of giving students feedback, so thinking of AI-generated feedback as one step in a process that later involves you giving feedback to your students. But well-designed prompts to AI can generate really useful and actionable feedback for students, and so that’s a treadmill kind of thing. And I guess the third big category I would mention is custom AI chatbots that are designed to simulate certain types of interactions or conversations for students, so that they can practice talking to a client or talking to a patient, or what have you. That’s a treadmill kind of use of AI, and a lot of faculty are having a lot of success with that, in part, because the fuzziness and unpredictability of an AI chatbot becomes a feature, not a bug, of that type of activity. The fact that it’s unreliable and contradicts itself is okay, because that often happens in the types of professional settings that we’re preparing students to enter.

Annette: One additional thing I think teachers are doing, which is super exciting, and I saw evidence of this in the faculty development seminar that I teach at University of Pittsburgh, is teachers are starting to basically vibe code their own apps that are supporting learning, which is just amazing to see, actually, because here is this opportunity to create this nichey little thing that is not going to be served by some big platform and subscription blah blah blah, it’s like I teach this particular really difficult concept. It draws on these different databases and government websites, and blah blah blah. We always have trouble with it. Students struggle to put this all together. It’s really hard to visualize, etc. Or you can ask Claude to make up a cool interface where students can play around with it, and then actually really get a good sense of this complex thing that I try to teach. Or here’s this visualization thing in a science class of we’re looking at a sample on their microscope, and you can change it into a diagram, and that can make it a little more easy for students to tell what it is, label the parts, things like that, and those kinds of real-time transitions across modes, say from image to diagram, or the interfaces. I hope that we see an explosion of those kinds of teaching tools that teachers are able to innovate with using AI on the back end, then, so, like, students aren’t directly using AI, but they’re interacting with the results of AI.

Marc: Yeah, I think for me the easiest way to start is to define AI’s role in the sort of relationship you have with your own teaching, and also set that up for your students to give it a specific role, develop rapport with your students, that level of trust as you go through this to see if this is effective in their learning outcomes. I am a little bit concerned about the fact too that some students have very expensive subscriptions to certain AI tools and services, while others are using the free plan, and what that’s going to do for assessment, and also equity and accessibility issues too, going forward. But you only get to find that out if they’re talking to you about this, if they’re doing this openly, and so when you have that rapport set up there too, like I’m doing right now, my advanced class about AI and society, my students are now given free rein to design things that they like with AI, after knowing about it too, and showing me and teaching me how they’re using these tools to learn, which I think is really cool, and as Annette mentioned, too, a lot of them are vibe coding now with Claude, which is just wild. When we were doing this six months ago, too, Claude really wouldn’t do a very good job. You do some very simple sort of things. Now we have some students vibe coding entire simulations of video games that are pretty impressive, and actual apps too. And teachers can do the same. So, once you’re comfortable with it, once you have that rapport there too, you can really start going into the creative possibilities about what AI can do to help. The challenge there is that there isn’t kind of a limit to it. It’s very hard to basically say where this learning stops with the technology when you can design so many different things based off of your imagination, and that is something to just kind of be thoughtful about, too, because it is a lot to just kind of press faculty on as well, especially if there are just one or two different use cases. Well, it’s this technology’s like clay, throw it on a pot, you can really turn it into anything that you want. And my response from some people have been like, “that’s a lot to take in,” like, “yes, it is.”

Rebecca: Just don’t forget to ask for accessibility features when you’re vibe coding,

Marc: Exactly, exactly. It’s a big thing.

Rebecca: So, we’ve talked a little bit about, well we’ve talked a lot about ways that generative AI might be able to be used to enhance student learning. In some of those discussions, we’ve talked a little bit about how it might also support teaching. Do you have any additional examples on how AI might be used to enhance our teaching?

Marc: Yeah, I’ll give you one that I’ve been using personally, that, like many faculty, I think I was in a bit of a secondary sprint here to try to bring my courses up to Title II compliance, and we got a reprieve that was sort of last minute, but it’s only a year-long rep, so we are still going to have to make sure our additional materials are fully accessible to be compliant with federal law. And I actually used a version of Claude to go through some of my course materials, and set it up to look at the different accessibility guidelines. I thought I’d done a very good job six months ago, and it turns out that no, I missed quite a few things, and it was actually very good to have that sort of experience for an AI tool to go through and sort of check your work. So, I think having AI as a tool that could sort of audit certain things – accessibility is one of them too – but also going through some of my course learning outcomes and seeing the ways that an AI system might look at that and compare it to a different university’s course learning outcomes. How up to speed am I and my peer institutions, and give me some options. I really do think if we were using AI iteratively, we can kind of make sure that we are the ones that are staying in the driver seat, if you will, and really center both not only student agency but in this case, teacher agency, to make sure that they’re making the right choices, and giving yourself some good options with AI to go through that process can be very helpful, and in my case, very eye-opening to me about these types of situations. I will say, my course, I had really done is one of my summer courses that I had not run in about two years. [LAUGHTER] I was just like, “oh yeah, I did that,” and no, I did not actually revisit that for accessibility concerns from two years ago. So AI can do quite a bit.

Annette: Yeah, like Marc, I’m very excited about the possibilities of AI to augment all of the details of teaching that we do, not the kind of human connection and the grading and the assessment and the reading and that kind of thing, but I think Marc’s example about how to make a course more accessible is excellent. The other thing I’ve been using right now is we have an institutional subscription to Claude, and I’ve used the MS 365 connector now, which basically hooks Claude up to my Outlook on my email, and what I found is having semantic search on my email is like a game changer. It’s just like I can ask questions of 15 years of emails and folders and all sorts of things, but I can ask questions of it that help me summarize conversations that are revived. It helps me right now think about my year-end report, like reporting that we have to do. How much time did I spend and what did I forget about the things that I did? Inevitably, you always forget things, and Claude is helping me locate those things right now. I have colleagues who have used Claude to design things that help fix the somewhat broken scheduling system that they have to deal with. It’s all these things that are super essential to the infrastructure of how a university runs, but there’s a lot of technologies that are either too big, clunky, they aren’t specific to whatever things that we need to do, and I think that I absolutely agree with Marc that the more we’re practicing these things as faculty, the better we are at understanding their limitations, their possibilities, and these are things too, like if I had a secretary, then I guess it would be replacing my secretary, but I don’t. This isn’t like undermining the intellectual work that I do as a professor. It’s not putting somebody out of a job, but it is really making my reporting more accurate and better. And so I think it’s important to recognize those possibilities.

Rebecca: I think that gets back to something you guys were talking about earlier, and using it to do some of the tasks, but it’s not the intellectual work of being a faculty member.

Marc: It’s also not the relationship aspect, too. That’s the big thing that, to me, again, part of AI awareness is just where are you going to put some boundaries in place, too. I use these tools all the time, like Annette does, but there are certain things I don’t want to do with my students. I’ve told them I’m not going to do this to write letters of recommendation, I’m not going to use it to answer emails, I’m also not going to use it to grade. Those are my sort of boundaries. Other people are going to be very different what they set up, but to me the best thing to do is just put yourself in conversation with that and start considering where you are going to draw the line and why.

Annette: And the other thing to note, too, that what I love about the way that Marc is talking about this is he’s really open with his students about that, like I’m not going to use it to grade, I’m not going to use it to do this, and I do think that as we ask our students to be transparent with their uses, it’s really important for us to be transparent with them about the uses that we have of AI, and I think they have some suspicions, they are frustrated, they’re interested. The students have all sorts of feelings about faculty uses of AI, and we need to take that into account when we use them. But I don’t think the students would care about me using AI in my email, [LAUGHTER] not for the writing of emails. I don’t use it for the writing of emails, but like for searching, like what are all the things I did for this committee for my department.

Marc: Now they’re not going to care about that, they’re going to care about the writing of emails. I agree, and you know the big thing to think about too, and I know we’re kind of dragging on the question, but this fall it’s going to be four years that high school students have had complete access to ChatGPT to generative AI coming forward. They’re not just going to be Plato exiting the cave, they never were entering our classes. They’re going to have experience using these tools, resisting these tools, and having that. And having this sort of conversation with them too, I think, is really important to think about their own agency when it comes to using or not using AI.

John: And the things you’ve discussed are ways where we can free up some time, so that we can have more time to devote to students, but there might be also some ways in which we could use AI directly to perhaps improve our teaching. One of the things I’ve done with faculty is have them use AI tools to help put their assignments in a TILT framework to make it more transparent to students, because we know what we mean when we ask students to do an assignment, but they don’t always know that, and that can be a nice way of evaluating it, to have an AI tool read it from the perspective of a student and put it in terms that may be meaningful, or perhaps to evaluate a syllabus for inclusive language and things that might get past us without the use of an AI tool, or use it to suggest some active learning exercises if we were mostly doing lectures, for people who want to move away from that, but aren’t quite as comfortable.

Marc: Yeah, absolutely. And there is an element of humility in that, too. You’re asking a system to help you perform your job, and that is something that I’ve talked with a lot of faculty about, that for them, they’re like, “no, I’m not going to do this,” but like, try it. Here’s an exercise, see if it is. And generally, there is that sort of aha moment when we do this in a workshop. They’re like, “oh, wow, so yeah, this is actually kind of helpful for me.” This is something I might take back and show to my colleagues and help them understand what’s going on too.

Rebecca: So we always wrap up by asking, what’s next?

Derek: I’m excited that the book is coming out this summer. That’s a big what’s next for me, I think, to help support that and to continue these conversations. I’ve been reaching out to a lot of the faculty whose work we mentioned in the book to have them on my Intentional Teaching podcast, and so I’m excited to continue some of these conversations in this audio format this summer.

Marc: Like Derek, I’m very excited about the book too, trying to plan what level of support that I can bring to faculty from a lot of these examples that we go out there to do lots of workshops and also lots of different engagements, both internally here at my university and also externally too. So happy to see that basically pop up and be out there in the world too, starting this summer for the ebook, and then the print book will be out at some point in time, early in the fall.

Annette: I’m excited, also like my co-authors, about the book being done. It’s always a big project, as Marc said at the beginning here, it’s a real sprint, it was a really intensive timeline. To take a commercial publisher and get something from conception to publication in less than a year is unbelievable, which is kind of the pace of AI, and it shows Norton’s priority for the book, too, which I was happy about. So, yeah, what’s next is, well, I’m working on an academic monograph about a long history of AI, and so I’m excited to do that. I feel like we learned so much in the process of writing the book, like all of the different worked examples from the teachers. I really am so grateful for the teachers that were happy to share those examples with us and with other teachers through this book. What I’d really like to do is I’ve been integrating some of those examples again with permission and workshops and things like that, and then to do that work at home too at Pitt and think more cutting edge about what we can do with the technologies that we have access to locally, and how I can work with the incredible colleagues that I have here at Pitt, and the students, how we might further student research too, which I’ve been working on here at Pitt too. So I’m excited about that next chapter, and I really am excited to learn more from other teachers, especially with the new innovations with vibe coding and all those kinds of experiments. I think we are on the precipice of some other hopefully interesting pivots in the ways that we approach education.

John: Well, thank you for joining us. We really enjoyed the conversation, and we’re really looking forward to seeing the book when it comes out. In fact, we’ve listed it as a reading group we’ll be running on campus in the fall, so we’re especially looking forward to seeing the final version.

Rebecca: Well, thanks. This was really great.

Annette: Thank you so much.

Marc: Thank you, guys. I appreciate it.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

[MUSIC]

445. Emotional Prosody and Online Learning

The use of instructor-narrated video slideshows has increased in response to both the growth of online instruction and increased use of flipped classroom teaching approaches. In this episode, Corinne Syrnyk and Alyson Kubat join us to discuss their study examining the impact on student learning of audio vs video narration and of the emotional tone conveyed by the instructor’s voice.

Show Notes

Transcript

John: The use of instructor-narrated video slideshows has increased in response to both the growth of online instruction and increased use of flipped classroom teaching approaches. In this episode, we explore the impact on student learning of audio vs video narration and of the emotional tone conveyed by the instructor’s voice.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

John: Our guests today are Corinne Syrnyk and Alyson Kubat. Corinne is the chair of the psychology department and director of research at St. Mary’s University in Calgary, Alberta. She is a developmental psychologist whose research focuses on social, emotional and mental health issues in children, language development, and human-animal activities. Corinne recently established the wellness opportunities and outcomes for pets and humans lab at St. Mary’s. Alyson graduated from St Mary’s last year with a BA honors degree in psychology. Kristin Croyle, the Dean of the College of Liberal Arts, Sciences, and Engineering here at SUNY Oswego, and a frequent guest on the podcast, will be filling in for Rebecca today, who has been called away for some other tasks by other administrators. Welcome Corinne and Alyson.

Alyson: Thank you.

Corinne: Hi. Thank you. Nice to be here. Thank you so much for having us. It’s very exciting.

John: Thank you for joining us.

Kristin: Welcome and today’s teas are… We always start by asking what our guests are drinking today. So are you drinking any tea today?

Corinne: I am not, but I think I will give you the stereotypical Canadian response and say that I started off with the Tim’s, which maybe is too idiosyncratic for your audience, but I went to Tim Horton’s and I got a coffee. But in terms of my preferred tea, that would be fennel.

Kristin: Ooh, nice. And Alyson?

Alyson: I have an Earl Grey.

Kristin: Ah, lovely. And today I’m doing iced tea straight up Lipton with raspberry, since we’re filming after lunch.

John: And I have an Irish Breakfast tea today. We’ve invited you here today to discuss an article that you had published in the November-December issue of Applied Cognitive Psychology entitled “The impact of emotional prosody on online learning in undergraduates.” To help frame this discussion, could you first explain what is meant by emotional prosody?

Corinne: So I think to kind of give you some context for emotional prosody, I’ll start by telling you a story about what we call prosodic speech, otherwise known as “Motherese.” That is essentially the way that we speak to infants. So, when you have a newborn baby, what you’ll tend to find is that the vast majority of parents, new parents, will speak to their kid in a very kind of exaggerated way. So, you have this newborn baby, and you don’t speak to them in a flat affect. You speak to them in an enthusiastic, lilted kind of affect where there’s lots of very clear starts and stops. So you might speak to a baby like, “You are so cute!” You’re relaying a lot of not just words they don’t understand, but the important thing that you’re relating there is potentially emotion and connection, because you’re having that kind of very positive emotional communication with the lilt of your speech, with the tone and the expression. So you fast forward away from prosodic speech to emotional prosody, essentially, that’s kind of the way that we speak, like I’m speaking right now. It has a cadence to it. There is a change in pitch and tone. Hopefully, you can kind of discern my motivation or enthusiasm for the topic by the emotion that I’m conveying through my speech. That is what we would call emotional prosody, and certainly a positive emotional prosody, as opposed to more neutral emotional prosody, which would be a lot more kind of like a flat affect. So if I was talking to you in a monotone way like this, which is a way that you would hopefully not speak to a person all the time, it’s that difference in communication that you’re receiving in terms of the emotional context that’s being communicated. Alyson, maybe you want to build on that?

Alyson: Yeah, I think it’s mainly just focusing on those acoustic cues that accompany speech. So a lot of tone, pitch, loudness of the voice, so all that kind of variance that accompanies speech and can draw interest in people who are listening to the person speak, and just kind of how that engages with the person that you’re speaking to.

Kristin: And what prompted your interest in applying emotional prosody to the online learning environment?

Corinne: Well, I think I’ll just clarify that Alyson was my honors student last year, and she did a really wonderful project. She got into her Honors Program, a competitive program, and I’m very picky [LAUGHTER] with my honors students, and I was very happy to work with her and right off the bat, Alyson communicated to me her interest in this topic, and she’ll tell you more about her own kind of personal ambitions and how this has informed her desire to work with me as a developmental psychologist. So Alyson, tell them your little story.

Alyson: Yeah, of course. I have a very personal interest in speech. I think it’s so interesting. I love learning about language development. I took a class during the… I believe it was the third year of my undergrad… focusing on language development, and the instructor was a speech pathologist. And I absolutely loved that class. It was so interesting to me. And when I went into honors, I just really wanted to focus on that interest in speech and language development. And I also am wanting to be a speech pathologist, hopefully. So I felt that that would be a good topic to focus on. I also, just through my learning, I found that most of my education was done through online environments, I suppose. I graduated high school in 2021, so it was during the height of COVID. So I found it really interesting just how quickly everything just turned into online learning just within the span of about a couple of weeks, maybe two weeks. And so I just found that so interesting, just based on the fact that there was quite a lack of understanding just about the foundations of online learning, about how it will impact learning in students, regardless of age. And I just wanted to kind of expand on that, just to be able to study speech in terms of online learning as well.

Kristin: Thank you.

John: And you began the discussion with the summary of some of the earlier research to set the stage for what the gaps are that your research fills in. Could you give us a bit of an overview of what studies have found before about emotional context and prosody in learning?

Alyson: So I think mainly previous research has focused on, as Dr. Syrnyk mentioned before, infant- directed speech, and just how crucial it is to the development of language in children, and just using that exaggerated speech, I find that’s where most of the previous research was kind of rooted in, considering prosody. And just prosody really aids cognitive processes such as attention, recall, and also promoting concentration. So most of that previous research just demonstrated that it possibly has implications for learning. So a study that I found really interesting when I was looking at previous research on this topic was a study conducted in 2022 by Dylman and Champoux-Larsson. They specifically focused on the impact of emotional prosody on the listening comprehension in, I believe it was 11- to 13-year old children. They found directly that positive emotional prosody promoted greater listening comprehension than when spoken to through neutral emotional prosody. I found there that there was just a very apparent lack of research on older students considering online communication. Yeah, so just the importance of emotions as well while communicating. Something that I found really interesting was considering academic emotions as well, and just how learners understand emotions in regards to their learning. A lot of the time, students felt that the emotions that they were experiencing while they were learning directly impacted their learning performance, as well as their social presence in online learning environments as well. They found, that in regards to social presence, when students perceived that there was a greater social presence in terms of online learning, that students felt that they would learn better, but they didn’t necessarily learn better. So I found all that to be quite interesting in terms of just building up the research for this study.

Kristin: Can you expand just a little bit? What do you mean by social presence?

Alyson: Oh, yeah. So in online learning environments, there was kind of an understanding that the facial cues and then the visual cues provided in online learning, where students were able to see the instructor, that would promote greater communication, and they would feel more connected with their instructor. But that definitely was not the case a lot of the times. So students felt that they were able to connect better with the content that was being spoken to them when they’re able to see a visual of the person speaking to them, but a lot of the times that actually interfered with their learning just based on the stimuli that was interfering with that.

Kristin: Mmhmm, kind of the number of things that they had to pay attention to.

Alyson: Yeah, it definitely impaired more than helped, even though a lot of students personally felt that that increased social presence would benefit, whereas it actually interfered with their learning quite a bit.

Corinne: Yeah, so like the social presence, this notion that somehow the instructor being visible is helpful is just kind of a wide held assumption. It really hasn’t been tested very much, which is part of the reason why Alyson wants to look into this as part of her research here. But there is a lot of mixed evidence in terms of the limited studies that exist in this field of interests. There is mixed evidence as to the efficacy of social presence. We don’t really have a hard yes or no as to whether it’s more beneficial or not, particularly when it’s combined with emotional prosody. So one of the kind of really neat things about her study is that she of course, got to kind of collapse these variables together by seeing what the impact of emotional prosody, whether positive or neutral, would be in impacting recall in terms of student performance, when you look at whether that social presence is activated or not. I also just want to take the opportunity to expand a little bit more on that Dylman, Champoux-Larsson 2002 study. That was Alyson’s kind of guiding light in terms of setting the foundation for her research. She came to me with that study very early and said, “I want to do something with this,” [LAUGHTER] and we really worked from that study quite a bit. Just to kind of build on that a little bit more. It’s really interesting that there’s a very limited scope of research when it comes to emotional prosody, and Dylman, Champoux-Larsson’s study is the one to go to. And what they found in that study was that they looked at younger kids, a group of 8- to 10-year olds versus slightly older, and the older were 11- to 13-years old, and what they did is they essentially tested them on five open-ended questions that were related to their content learning in their classes, which was grade appropriate. And from that, what they effectively found is that emotional prosody was important for the older kids, the 11-to 13-year olds, but not so important for the younger kids. When you think about the developmental profile of coming from a place of prosodic speech being important to kind of maybe having a little bit of a U-shape across middle childhood into later childhood.

John: I think the study is especially interesting given how much more important online learning has become, as well as even in classroom learning, more and more instructors are using videos and using flipped learning approaches. So this applies to a growing share of the instruction that’s taking place. Could you describe the design of your experiment, the four conditions that you used?

Alyson: Yeah, absolutely. So in terms of the four conditions, we were comparing positive emotional prosody versus neutral emotional prosody, and then we were also comparing the delivery conditions, the delivery lecture conditions, so it was a video condition and a audio condition. So based on that, we looked at the interaction between all of those conditions as well. So all participants were shown a 11-slide PowerPoint, and then within that, they were either presented with a video version or an audio version. So the video version would have a video of the instructor teaching on the upper right-hand corner, whereas the audio condition, they were just presented visually with the lecture slides.

Kristin: Nice and tell us a little bit about your participants.

Alyson: So the participants were 80 undergraduate students at St. Mary’s University, and within that there were 20 students assigned to each condition group. They had a mean age of roughly 20 years old, and they spanned all of the degree programs within the school.

Kristin: And I know somebody is going to want to know what the lecture was about. So tell us what the lecture was about, the 11 slides.

Alyson: Yeah. [LAUGHTER] So the lecture… pretty much when I was looking for a topic for something to teach, I wanted it to be something that could be taught relatively quickly, within the five- minute time period that I was looking for for the lecture. And I also wanted it to be something that was slightly more of a niche topic, which proved to be a bit more difficult, just in terms of finding a topic that I felt would work well under those kind of guidelines that I had set out. So I eventually turned to astronomy, and I looked at astronomical eclipses because I felt that was slightly more niche that the majority of students wouldn’t have been previously taught that topic, whereas, while I could still kind of maintain that five-minute duration while being able to include all the information that I wanted to quiz them on.

John: So what did you find when you compared the outcomes in terms of how much the students remembered from these videos?

Alyson: So we assessed them based on a 10-question multiple choice quiz. So once they watched the lecture video, they would complete that quiz directly after. And we found that in terms of the results of the study, that the participants who had been given the positive emotional prosody condition performed significantly better on the quiz than the students who had the neutral emotional prosody so even just comparing that, the positive emotional prosody condition had a mean score of 72.25% on the quiz, whereas the neutral emotional prosody conditions had a mean score of 56.5%. And we found there was really no significant difference when just considering the delivery of the lectures, comparing audio versus video. But the interaction was quite interesting that we ended up finding. So I had initially assumed that combining positive emotional prosody with the video lecture, just due to that potential increased social presence, would result in a better score on the quiz. It actually turned out that while emotional prosody, when conveyed positively increased the quiz scores, it was actually that audio condition that resulted in increased grade scores compared to all the other conditions, which I thought was quite interesting.

Kristin: So if there was positive emotional prosody, if it was the positive emotional prosody condition, they had significantly higher scores on the quiz, as long as there was no video, yes? Did I get that right?

Alyson: That is right. Yes. [LAUGHTER]

Kristin: It sounds like it was a surprise. I know that you have some explanations about why that might have been the case. What do you consider to be the most promising explanations?

Alyson: I think overall, I found the cognitive load theory to be quite helpful with explaining that interaction, and it was really just focusing on the amount of information that was presented to the students and how that impacted them cognitively, in terms of what they were able to retain. So I think for most of the students, it just kind of helped that the audio condition was in combination with positive emotional porosity was interesting to them, enough to engage them cognitively, but it wasn’t at the point where it was exceeding their cognitive threshold, where it would be negatively impacting their cognitive processes such as attention and recall and comprehension, whereas I think it was the video condition that really was just too much in terms of cognitive threshold, and it just exceeded that threshold and eventually just negatively impacted their grades.

Kristin: So kind of a sweet spot of attention without too much, without going over in terms of what they were being asked to pay attention to, both the facial expressions of the instructor, for example, and the content.

Alyson: Absolutely, yeah.

John: One way of stating this is that the video of the instructor was distracting them from the content on the slides?

Corinne: Yes, exactly. And I think I’ll just give you a bit of a definition for cognitive load theory. It’s essentially where we get a limited amount of information that we can process and that varies from individual to individual, of course. T here’s a normal distribution that most of us can maintain attention, maintain active learning with a certain amount of information, some of us more so or less, but we each have these individual thresholds that are reached, and when that threshold is reached, that’s essentially your working memory bench or table is full, and you can’t really process much more. So your ability to attend and comprehend learning what’s going on, it’s kind of maxed out. So yes, you’re quite right, John, like this notion that there’s this point by having that additional information of the instructor visible on the slides was just too much, and one way to conceptualize that is through this notion of distraction. When you have too much information, it is distracting and you can’t absorb the information that you probably could if there was less information there.

Kristin: I know that your study doesn’t go this far. I expect that people who are teaching online, who are using a lot of video, that they would argue that there are other benefits that they’re getting from that, that they’re, for example, trying to form an individual relationship across space and time, and that they’re using it instead as a motivational tool, or something like that. I know this is beyond what your study looked at, but what are your thoughts on that?

Corinne: I think that’s a great point. This is part of what Alyson and I were interested in. There’s this kind of general… ever since COVID, particularly… this general assumption that being visible is better, like as an instructor, but we have very little evidence that it’s real. In fact, this study kind of shows the opposite pattern of effect. So, in terms of adding the video, the video yourself, as John says, might be a distraction. This study is a small study. Of course, it needs replication. We need larger sample sizes, of course. But what I tend to see in terms of the online learning world, I’d like to see more evidence-based pedagogy, particularly when it comes to things that are related to like attention and memory and recall, because those are things that we naturally depend on, we take for granted in a classroom environment, because you can see your students and you can engage with your students, and you can tell if somebody is on their phone or asleep. But in an online environment, of course, that feedback is often one way, and the assumption is that, well, if they see me while I have these slides on, this is going to be better. But do they really need to see you? Maybe not. Do they really need to hear you speak with some enthusiasm or emotion when you’re speaking? These results seem to support that instead.

Kristin: I’m struck, because right now, in this environment, the listeners for the podcast are going to be listening to people with positive, emotional prosody and no video. But right now, as we’re recording, we’re using a platform in which we can see each other on screen. So we’re doing our best to replicate your conditions [LAUGHTER] right here at this moment. We’re trying to leave out the negative, the lack of emotional prosody though.

Corinne: There’s so much scope for more research here, really, which is really exciting about Alyson’s work here, is that it’s really just scratching the surface of what else we could really be examining within this area.

Kristin: The other thing that it brings to mind for me is research on distracted driving. That conversation in the car is less distracting than a phone conversation, for example, partly because the other passengers recognize the driving conditions, they can see if you’re entering a difficult driving situation. That perhaps it speaks more to the actual reality of being in the same place and time in which that personal connection makes a bigger difference. Perhaps there is something different about the online environment where we’re trying to create an individual connection, but at the same time, there is an artificiality to it, and prioritizing student learning over our assumptions about what might be creating a connection may be a more supported way to go.

John: I was really impressed by the magnitude of the effect, not just that it was statistically significant, but that was a really large difference in the scores. That result is remarkably strong.

Alyson: I was quite surprised by it as well. I think I was expecting there obviously to be some difference, but when I calculated all the quiz scores, I was genuinely surprised by how significant of a difference it was, especially when just considering the fact that it was a five-minute lecture video and immediately followed by a 10-question multiple choice quiz. I really wasn’t expecting the results to be that significant, especially just considering, yeah, like you said, the lack of research, the lack of basis that there was in terms of providing significant support, I suppose. Whereas it was just kind of surprising how significant that difference was.

Kristin: I’m guessing you triple, triple checked it. You’re like, “what?”

Alyson: I did, yes. [LAUGHTER]

Kristin: Two full questions better, yeah.

Alyson: Yeah, exactly. It was just very, very surprising. But it was really interesting to go from watching these people engage with the study and then have actual results to follow that I found that to be quite interesting.

Corinne: Alyson did a really strong study, I’m really proud of her. She had a really strong design, and she was really committed to recruitment and seeing through all the very important aspects that are required when you’re conducting research. And she did such a fine job that we are so happy to be able to go ahead and publish her work, and work with people like you as well, so that we can share these results.

John: And we should thank Michelle Miller, who wrote this up in her substack earlier, which is how we found out about this paper. We’ll include a link to your study and the studies you cited, as well as to Michelle Miller’s substack post on this, for people who may want to follow up a bit more. Going back to Kristin’s comment about how our podcast is audio, I did hear a statistic, actually, from some of our colleagues at Finger Lakes Community College this week, where they noted that 40% of podcasts have moved to a video format. We have chosen not to do that for a number of reasons. The main one is we mess up a lot, [LAUGHTER] and this way we can edit out all the mistakes before we release it, but also, we like to listen to podcasts with just the audio, and both Rebecca and I at least, generally don’t watch videos quite as much because it takes more time to sit there and watch the video, when with a podcast, you could be driving, you could be walking, you could be doing other tasks. For us, we’re probably going to stick with it, even though we do see more and more podcasts moving to a video format. But at least now we have a little bit of empirical evidence to support the continued use of audio only.

Kristin: And we always end by asking: “What’s next?”

Corinne: Well, like I said earlier, I think that Alyson’s study here really just opens up a whole can of worms for all sorts of very exciting new leads into research. One of the things that Alyson and I certainly have been discussing is we’re assessing this in terms of student recall or performance on a task, but it would be really interesting to look at how students perceive themselves, emotional prosody, within online learning mechanisms. Because I think it’s something that I would assume that most students pick up on and have preferences based on the emotional prosody of their instructors, and that would be reflected in terms of whether they think that instructor is a better quality instructor or not. And what does that mean in terms of the content that they actually relay? And this is another thing that we were thinking about, which was really maybe a little bit off the wall, but kind of interesting. You’re familiar with the uncanny valley phenomenon, right? And we’re thinking, what if there’s like, an uncanny valley for emotional prosody? What if trained people who are more neutral on their emotional prosody. How would that be perceived by learners or by people listening? Would they be convinced necessarily, or would they be like, “Oh no, there’s something going on here?”

Kristin: For listeners who may not be familiar with the uncanny valley. Can you quickly describe it?

Corinne: Yeah. So the uncanny valley phenomena is this notion that humans are essentially kind of very suspect [LAUGHTER] to robots. We have a high sensitivity to the artificial. We’re very kind of good at sussing out what’s artificial versus what is not. And that is found across a range of kind of things in the world. So whether it’s robots or different kinds of aspects of learning modalities, we’re good at picking up on artificial versus real. And this is an effect that’s evident in infancy, like it’s evident from very early on. Very young children will be able to discern inanimate versus animate objects, inanimate versus animate movement, etc, physical properties. And this extends into, of course, you know, as you get older, but in terms of an uncanny value, and when it comes to emotional prosody, and not all instructors are built the same. You know, I think about all your university profs. I’m sure we all had University profs, I’m one of them, that had a more kind of flat effect when they talked, they spoke more monotone and they kind of talked like this. They probably had a reputation for speaking a little bit more monotone, but in today’s world in higher education, there’s certainly a lot more emphasis on pedagogy, on teaching and improving skills and attributes of our instructors, which is great. But can you teach emotional prosody? Can you teach someone to speak with more emphasis and cadence and expression? And if you could, what impact would that have on people?

Kristin: Or could you essentially auto tune them into it in an online environment? Would that be perceptible? How would it be perceived by students if they knew or didn’t know? I mean, we see what auto tune does to music, it’s used sometimes in the background and is an embarrassment, if people find out, other times in the foreground, as a musical technique. With all the tools we’ve got, certainly there would be a way to manipulate it.

Corinne: Absolutely. Fascinating, you can do all sorts of things, and with AI now too, I mean, right? Like we are in this weird world of we’re not quite sure what’s real anymore or what is and I don’t know exactly what that would do to things. You could probably try to set up different aspects of non- real people doing this kind of thing now, and test that in terms of perception and learners or people in general.

John: And ElevenLabs and some of the other AI tools have developed some very realistic, emotional prosody in terms of their speech. And it would be interesting to do an experiment when people are faced with AI avatars delivering that versus perhaps actual instructors. [LAUGHTER] Because we do see AI avatars being used increasingly in a lot of social media, a lot of ads and so forth, and I wouldn’t be surprised if they started showing up in some online instruction.

Corinne:Yeah, kind of terrifying, [LAUGHTER] for a number of reasons. But yeah, there’s a lot of questions there. And I mean another thing, I love research, so when I started thinking about all the other things, you know, it gets really exciting. But like, at what point does emotional prosody compete with content? Like, there has to be some type of like, I would assume there has to be some type of U-shape there in terms of that effect. I kinda think about, like, people who are very convincing speakers in terms of the way that they speak with that emotional prosody, but the content is incongruent with the actual delivery. How is that interpreted? What impact does that have on recall of the message that they’re giving? So yeah, lots of really cool things to explore.

John: And I do have to say, when I was reading this, one of the things I was thinking about was the movie Ferris Bueller’s Day Off, and that little video clip, that little segment in the middle where Ben Stein was talking about tariffs and how harmful tariffs could be in the economy. And apparently that message never got through to students, or we wouldn’t be in the current situation we are in the U.S. Maybe it’s because we tend not to have enough emotional prosody in our economics classes.

Corinne: Well, I want to say anything, but, [LAUGHTER] yeah, there’s some weird stuff going on there. [LAUGHTER]

Kristin: That is really funny. [LAUGHTER]

John: And so Alyson, what are you doing now?

Alyson: I am currently working as a 911 dispatcher with EMS. I am hoping to apply to graduate school in the next year or two to kind of pursue that. I am hoping to volunteer as well within the next bit, and also just hopefully to engage in some future research opportunities. I think just working with Dr Syrnyk through this whole experience has been very helpful in terms of mushing together my education, I suppose, in terms of all the courses I’ve taken in the past about research. It’s really shown me how important research is, and especially the discussions that can result from research that occurs. So I think that it’s just really made me quite passionate for future research opportunities that I could possibly have, where the research that we’ve done here could possibly lead. I think that would be really interesting to see.

John: And you’re off to a great start already doing some research that can have a significant impact potentially on how people teach online classes and how students learn. Anything that we can do to help improve that would be really useful.

Alyson: Yeah, absolutely. Especially just as online learning becomes increasingly prevalent in university classrooms. It’ll especially be interesting to see how different forms of lectures are incorporated into online platforms. There are some classes that I’ve taken, like a statistics course, where it’s very much more involved. It’ll be interesting to see how that kind of forms from where it is now into just based on research, distant into the future.

John: Well, thank you. We really enjoyed talking to you, and thank you for sharing your research and this discussion of your work with us.

Kristin: Yes. Thank you both.

Corinne: Yes. Thank you for having us. It’s been wonderful and nice to get to know you a bit and see you. It’s just been a great opportunity.

Alyson: Yeah, thank you so much.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

[MUSIC]

428. The Science of Learning Meets AI

The widespread adoption of and the rapid evolution of generative AI platforms have created substantial challenges for faculty in how we assess student learning. In this episode, Lew Ludwig and Todd Zakrajsek join us to discuss a new resource they have created that is designed to help faculty use AI to efficiently support teaching practices based on the science of learning.

Lew is a Professor of Mathematics at Denison University, where he served as Director of the Center for Learning and Teaching from 2020 to 2025. Much of his recent work has focused on innovative methods for utilizing generative AI. Todd is an Adjunct Associate Research Professor at the University of North Carolina at Chapel Hill, from which he just retired a few months ago after 17 years. He is also the Director of the International Teaching Learning Cooperative and the Director of four Lilly conferences on evidence-based teaching and learning. Todd is the author of many superb books, and has published six books (so far) in the past five years.

Show Notes

Transcript

John: The widespread adoption of and the rapid evolution of generative AI platforms have created substantial challenges for faculty in how we assess student learning. In this episode, we explore a new resource designed to help faculty use AI to efficiently support teaching practices based on the science of learning.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guests today are Lew Ludwig and Todd Zakrajsek. Lew is a Professor of Mathematics at Denison University, where he served as Director of the Center for Learning and Teaching from 2020 to 2025. Much of his recent work has focused on innovative methods for utilizing generative AI. Todd is an Adjunct Associate Research Professor at the University of North Carolina at Chapel Hill, from which he just retired a few months ago after 17 years. He is also the Director of the International Teaching Learning Cooperative and the Director of four Lilly conferences on evidence-based teaching and learning. Todd is the author of many superb books, and has published six books (so far) in the past five years. Welcome, Lew, and welcome back, Todd.

Lew: Thank you.

Todd: Thank you, Rebecca,

John: Our teas today are:… Lew, are you drinking tea?

Lew: I am. I’m usually a coffee drinker, but I have a lovely Cha Atiso. This is a Vietnamese artichoke tea, which a student brought back for me from the summer, and I’ve really, really been enjoying that. So that’s my tea for today.

Rebecca: What does it taste like?

Lew: It’s got a little sweetness to it, which I like. I don’t put anything in it, but I really enjoy it. And it’s supposed to be healthy, no caffeine, all that sort of stuff.

John: And Todd, are you drinking tea?

Todd: Yeah, I went with a roasted dandelion spice tea. It says detox on the package, and I can always use a good detoxing. So I’m doing that and sipping it from my favorite cup. Mmmh.

Rebecca: That’s right, the tea for teaching mug.

Todd: I have my tea for teaching mug here. I never leave home without it. Actually, I never left it home. [LAUGHTER]

John: And Rebecca?

Rebecca: I have a Swiss Breakfast tea, John.

John: Very nice… on a chilly morning here in upstate New York. And I have a ginger peach green tea today.

Todd: Wow. Quite a variety.

Rebecca: Nice, this big variety. So we’ve invited you here today to discuss your newest book project, The Science of Learning Meets AI: A Practical Faculty Guide to Purposeful Integration, Student Engagement, and Ethical Practice. Can you tell us a little bit about the origin of this book?

Lew: Yeah, sure. I can get us started on that. So as you know, Todd and I both have backgrounds in teaching centers of faculty development. And our national conference is something called POD, Professional Organizational Development Network. And in 2024 we were at that conference, and here ChatGPT had already been out for about two years, and we were still kind of struggling with the idea of, how are faculty supposed to use this? A lot of the questions we were getting is, “Hey, how can I use AI in my classroom?” And it wasn’t until this conference was up in Chicago in 2024 where we realized we were asking the wrong question. It’s not really how to use AI in the classroom, but instead, maybe we should be thinking about, why are we using AI?

Todd: Yeah, and actually, I’ll jump in too, because Lew went right to that technical part and everything. Another thing that happened, which is why I love it when people get together, is Lew asked me if I would help out or just talk to him a little bit about some of the science of learning, because he wanted to really work on this project with bringing AI into this, explaining why it’s important to use it. And I said, “Well, that’s interesting, because I just started to outline a book that I want to do on AI, and I was going to check with you about the AI part, because I got the learning part down.” [LAUGHTER] So we just said, “What the heck, we’ll do it.” And Lew didn’t know me very well at the time, so he said, “Yes.” [LAUGHTER] Anyway, we made it through. But the big thing was that we really wanted a book that would teach faculty and help them understand the learning theory behind why the learning happens and everything else, and use AI to augment that. So this isn’t like using AI to do things, it’s using AI to help us do the things that we’ve been doing.

John: You start the book by acknowledging that faculty didn’t ask for this AI disruption or for any of the evolutions of this. Why start with this instead of with excitement about AI’s potential?

Lew: Oh, it’s called learning from one’s mistakes. So again, ChatGPT came out in November of 2022, by August of 23, I remember on my campus, we had a fairly large across the board faculty meeting about AI. It was about a half-day session. So administration was there, IT was there. And as the Teaching Center Director, I gave probably a 90-minute session on, “Hey, here’s this new technology and how we might be able to use it in our classroom.” And what happened was, just by saying, “Hey, here’s how we might be able to use it,” some folks labeled me as AI pro. So I was just pro-AI, hey, you talked to Ludwig, and he’s just going to be all in on AI. And that was almost like my scarlet letter [LAUGHTER]that I was wearing around campus to some folks. So I learned from that. So any of the workshops, any of the things I do since then, I start off with this exact phrasing, just to kind of level the playing field here, I say “You didn’t ask for this. You didn’t request an unregulated, untested, and rapidly evolving technology to suddenly upend education and nearly every other sector of society.” Now, I’ve been delivering that, like I said, quite a few workshops. I did it back in August for a group of high school teachers in Richmond, Virginia. They applauded me. [LAUGHTER] So it’s just kind of getting it out there and acknowledging it that, yes, this is a disruptive tool. We kind of have to acknowledge that grief and then kind of move on from there.

Todd: Yeah, I would say too, is really, I really thought Lew had a good handle on that part of it. I wouldn’t have put that in there, quite frankly, for the book here, and Lew was pretty insistent that it go in there. But as we started talking it through, I realized I always started any COVID workshops this way. When we were doing emergency remote teaching, if you just started talking about emergency remote teaching and didn’t acknowledge just all of the stuff that happened with COVID at the time, on the number of deaths, for instance, you can’t just jump into something and ignore it. So I think that Lew had a really good point here, and then I started pulling together what I did with that, with what he was talking about. And I think the whole concept is we started thinking within the book of, how do we do this? To recognize people are grieving, that they’re struggling, and we say it’s hard that we see you there, the frustration is real. Teaching is changing, just as it did with COVID. We don’t have a choice of that, and AI isn’t going to go away, so let’s recognize what’s going on, and recognize even some of the impact of AI, and then from there, then move on with a sense of acknowledging what’s going on.

Rebecca: It’s almost like you’re modeling some good teaching practices there. [LAUGHTER]

Todd: Kind of. Periodically it happens.

Lew: We try.

Rebecca: This adapt, create, embed, progression, the ACE model, is used throughout the book. Can you talk a little bit about what this looks like in practice for a faculty member who’s never used AI?

Lew: Yeah, so this work kind of, again, developed that starting point back in November of 24 after we had that initial meeting in Chicago and started to realize we needed some early wins for faculty. So we got a lot of faculty out there who are kind of hesitant about this new technology. And if you gave them something like a prompt or something that didn’t work, they’d be just like, “See, I told you, this AI thing, it’s a disruptive thing. So we’ve been workshopping this quite a bit early January onward, and refining the model. Originally, you ready for this? It was, instead of adapt, create, embed, it was improve, create, integrate, which, as an acronym, is icky. [LAUGHTER] We didn’t think that would land very well, so we went with ACE instead. So for example, the adapt, that’s kind of the easiest win. So what you do is, you take an existing assignment, you have that assignment that it’s a big project, but for some reason, it just doesn’t land with students. They always hand in something a little bit different than what you asked for. Well, that assignment’s begging to be TILTed, right? So Mary-Ann Winkelmes has this idea of transparency in learning and teaching, and it’s a technique that you can do for your assignment to make sure that your students understand why they’re actually doing the assignment. And it’s a kind of a three-step process. And AI is great at TILTing your assignment, and faculty members can take an existing assignment that they have. They’re experts on that assignment. AI can help them TILT that with this framework, and then they can see how that might better fit into their language for their course, and help improve what their students are doing. And the TILT framework has been really good. It kind of raises all student boats. But in particular, it does well for underrepresented and first-gen students do exceptionally well with this. So again, just kind of an early win with the adapt model.

Todd: Should mention quickly that we kind of started out and we based a lot of this on Ruben Puentedura’s model of SAMR, which is the substitution, augmentation, modification, redefinition for all of technology. If you have any new technology out there, you started with substituting it. How can we do this instead? You can augment it. And that’s a really interesting model. Didn’t quite work with what we were after. And so Lew had lots of workshops he had been doing. And so we kind of used that as a foundation. But as Lew just pointed out, the adapt, which would be similar to that original substitution, is to get you rolling. And I’m going to go ahead, Lew and take I’ll take the other two real quickly. So next is create. And essentially, it’s looking at how you can do something that you really couldn’t do before. And there’s lots of things that are out there that we could use for this one. We’ll talk a little bit more about those, probably later on, as we’re looking at some specific examples. But when you think about what you can do and couldn’t do before, one quick thing is a daughter, one time, was struggling with something, and she said, “Can you help me?: And I said, “I’m busy right now. I can help you a little bit.” 15 minutes later, I said, “I can help you now.” She said, “Oh, took care of it.” I said, “How’d you do that?” She says, “Oh, I just got on a group, a small online group, and then we solved it together.” And I said, “Who was it?” And this was years ago. And she said, “Oh, there was a student from Germany, one from South America, somebody from Nebraska, and myself.” That was never possible before. And so many things in AI do things like that. They’ll set up things we could never do before. So the “create” is, how can we do things different? And then once you’ve adapted it and created it, the next thing is, just embed it. Just make it part of the regular aspects of your course. How do you do that? And what we like to point out over and over again is that, you do this wherever you’re at. If you’re just getting started, you just adapt some things you’re doing. And that’s where Lew is really good at setting some of those things up. If you’re a little bit nervous, just take something you’ve always done, do it with AI, do a little bit better, faster, and then just get started, but don’t get overwhelmed with it.

Todd: And the other thing about doing with something you’ve already done is then you’re the expert, and you can critique what AI is doing for you. I wouldn’t go off and learn a brand new subject you learn using the AI, because I’d have no idea. But again, coming from that wheelhouse of your own expertise, don’t forget you are the expert with the content as well as your students and context. So keep that in mind whenever you’re dealing with AI.

John: And that example you use of the TILT model is a good one. I’ve given workshops to faculty about it, and a lot of people were reluctant to put in the effort to make all those changes. But when you show them how AI can do that, or better yet, if you share perhaps a prompt, or if you share a GPT or a chatbot tool that they can plug in their assignments to and their learning objectives, and it can do it for them, or at least give them a starting point on it, it makes it so much more likely that people are going to do that. So you talked a little bit about the ACE model, which is used throughout the book. But could you tell us a little bit about the organization of the book?

Lew: Yeah, sure. So the book’s kind of broken up into three parts. The first part, we call it “Creating your own Gen AI roadmap.” And again, chapter one, you didn’t ask for this, but here it is. So just kind of, again, acknowledging that situation that we find ourselves in and how to move forward with it. Then in chapter two, we start talking about what it means to think about shifting our teaching in the world of generative AI, so the idea of being transparent with our students and how we’re using it and setting up good communication with them, and then also the idea of as faculty members, as we’re embracing this, we really have to be curious and flexible as we work with this technology. We’re always pushing our students to take risks, be flexible, be curious. Well, guess what, that shoe is going to now fit on our foot as we try to move forward with this model.

Todd: So that kind of got things up and running, and then we kind of ran right into the learning aspect of it. Just to give you an idea of the book, I guess is probably the easiest way to do this is. Chapter four is on universal design. I’ve been a long time advocate of universal design, and I think AI can be now done to do things that were really challenging before. So we did dedicate an entire chapter to universal design. Then chapter five, we really thought community was important. So chapter five is about building classroom community and trust, looking at belonging, engagement, and integrity, kinds of issues. Chapter six comes into backward design, a huge aspect of teaching and learning, kind of knowing where you’re going as your goal. And then chapter seven is looking more at learning science of cognitive load, attention, spacing, interleaving, retrieval practice. And that’s the “building the foundation section.” And the last section of the book, section three, is for kind of deeper learning. We looked at some things about teaching and learning that just we know works well. So chapter eight is guiding students from novice to expert. We put together in that section zones of proximal development, scaffolding, and the gradual release of responsibility as ways of helping students kind of go into deeper aspects of learning. Chapter nine is on strategic learning. So we looked at transparency, TILT, cognitive apprenticeships, metacognition and those types of issues. And then we wrapped it up with chapter 10 of persistence and motivation. So there we pulled together the concept of self efficacy, growth mindset and self-determination theory. So the chapters have two or three strong learning principles to them, but they’re put together in ways that not only help explain the individual things, but also overall this concept, like persistence and what kinds of things go into that. And then the last thing I’ll say, since I’m on a roll right now, is overall, I think we really thought it to be helpful, repetition is just helpful for learning, right? We know that. And so we did is we built a book that has strong repetition. So chapter after chapter, we basically started with chapter four, and then we go all the way through chapter 10, and it’s set up so that there’s always an opening story or anecdote to set it up, which we’d want to do for just getting the learning focus there. Then from the model, there’s an improve activity, and then we look at the science components, and from there we might be looking at metacognition or Bloom’s taxonomy or something. So that comes into the second two parts of the model. Here we have a create and then embed, those activities are in there. And then we talk about the ACE model, and we have more activities that come into there. Then we look at risks and guardrails. And again, we’re going to acknowledge everything has risks to it. We’re not going to just pretend like it’s all great. And then we have a conclusion, pull it together, and we finished every chapter with discussion questions and reflection questions. So if you’re a faculty developer and you want to use this as a book group, for instance, we’ve got your discussion questions already built in, but for having faculty work through it, there’s reflection questions in there. The last thing I’ll mention is that we talked really extensively about how to cite this stuff, big APA kind of person, but we really wanted this readable, and so we went with a model where we put everything in as end notes, so as you read through it, you don’t get the standard citations with dates all the way through it, it just reads like a novel, almost, and I think it’s really easy to read. So you don’t have to read it cover to cover, although you should, [LAUGHTER] maybe three times. You can just go through and pick out what you like, but if you do go through, we’ve actually built it so that you start with simple kinds of examples and move to more complex ones. And Lew is masterful at putting together how to like, ask questions and do prompts and specifically with a separate font in there, we just have paste this in and add your components, and it spits out the answer. Pretty cool.

Lew: So what Todd’s explaining there is, we have a lot of different techniques. We have things called activities as well as examples. So each chapter has six activities, which are kind of page-long discussions of how you might use AI. And it’ll start off with some prompting, like he said, in that different font, but you put it in your context. We don’t do this: “Oh, a biologist would do this. Now you do it for yourself.” You actually, in the context of this thing, put in your information so you’re directly dealing with it. So we have, like I said, six of those for seven chapters. So 42 of those things. Each one could be a workshop. And then also we have smaller examples for seven chapters, we have 12 smaller examples, which is another 84 prompts. Those aren’t prompts, those are just more ideas. But Todd mentioned this idea of meta prompting. We give you a way to turn those examples into a prompt for yourself. So you’re using AI to create your prompt for you, and I think it’s called meta prompting, so that’s what he was referring to there.

Rebecca: There’s a lot written about the science of learning and Todd, obviously, you’ve written some books on the subject. How did you decide what science of learning concepts to include?

Todd: I just put a whole bunch of stuff in there. And then Lew started saying, “Nope, nope, nope.” [LAUGHTER] No, we got along great on these things. It basically boiled to Lew does tons of workshops, I do workshops, I think I’ve been at 300 campuses so far. So I looked through the different presentations I’ve given, and when things kind of hit and when they didn’t hit. Cognitive load is something a lot of people just don’t quite understand, and is a huge foundation of learning, because within cognitive load, you’ve got automaticity and schema activation and all kinds of different things that are practicing retrieval kinds of things. So I just went back to the warehouse of stuff and picked the things that worked, which is what I still think is really, really valuable about the book, is that these are things that work, and it’ll help explain them as you go. Big things like the metacognition, Bloom’s Taxonomy, cognitive load, automaticity, practice, retrieval, interleaving, the kinds of things we’ve heard. And it was hard, I will say, Rebecca, it was really hard. There’s a lot out there. I mean, obviously I got what is it a 270 page book on learning. So how do you just fold in some of that? And so, it doesn’t have everything, but I think it has the big things.

John: So you mentioned before that AI allows us to do things that we couldn’t easily do before. Could you give us an example and walk us through that in terms of how AI could be used to create learning activities that could not be done as easily in the past.

Lew: Sure, individualization of instruction. I’m at a pretty small school here at Denison, but I could imagine, wow, what if I was teaching a class of 400. So I might end my class here at my small school with, you know, a muddiest point, I get 20 responses. I look those over, what did students struggle with that day? And then next class, I can kind of address that. Imagine if you had 400 students, what could you do with that? Well, now, with AI, you can take this classroom assessment technique of muddiest moment and, on the spot, have students type their prompt into, say, a Google form or something like that, and as maybe the students are off working on some kind of project or discussion together, within two or three minutes, you could use AI to organize those muddiest points and give me the top three things that these 400 students are struggling with, something that was just was not even feasible before. So I think you know, at scale, this thing can be really useful for those types of examples. And with regards to CATs, the classroom assessment techniques, Todd’s got a book of like, 50 of these things, right? So there’s so many things that you can kind of do on the fly that AI can then help you with, kind of the managerial stuff, just the paperwork thing of keeping track of that stuff so you can respond in the moment for what your students are doing.

Todd: And Lew, I appreciate you mentioning that book, it’s interesting, got me thinking real quickly here. So that book is Classroom Assessment Techniques, which not too many years ago was like the standard. Everybody had a copy of this thing, and they did because the first edition was 1988 I believe it was, second edition was 93 though, and in 93 was right about the time we started talking about learning versus teaching, and how would you know if students are learning? And along comes Angelo and C ross, their book was out, I guess, already, and so it explained it. I mentioned this quickly because I then worked with Tom Angelo to do the updated version, the third edition, which is now out 30 years after the second edition. And the second edition was still selling, but we were removing things like “hand out papers to the students, have them respond on the muddiest point, and then take them home, but make sure you don’t ask questions that you don’t have time to respond to. Don’t ask elaborate things because it will take too much time.” Lots of stuff about CATs was about being careful not to overwhelm yourself. And if you think about that in the old days, that’s what you do. Pieces of paper were handed to, you’d read through them and everything. So we took out things like “share your results on the overhead protector,” and those are gone, and then we replace them with things like Lew was saying is, just put your students into a small think-pair-share, have them respond to this thing with a PollEverywhere or something, and then while they’re talking, then come up with the four muddiest things going on, and then teach those in the second half of class. And so, all of those classroom assessment techniques that we’ve been using for 30 years can now be made easier with AI. So I’m really excited about this.

Rebecca: You mentioned earlier about the chapter on Universal Design for Learning, and I know you’ve been a long time advocate, Todd. Can you talk a little bit about how faculty can use AI to design courses that are better for diverse learners and really have kind of access in mind, rather than retrofitting accommodations.

Todd: Yeah. So number one, I think the thing to point out here is, so I teach, this Lilly teaching conferences. They’ve been around for a long time. There’s different types of sessions that I’ll include in the program, and some of the sessions, and I talk to the presenters about this all the time, some of the sessions are just hot topics that you know are just going to go: engaging students in asynchronous classes, it’s going to fill a room. How to do active learning strategies, going to fill a room. They always have. Sometimes you have topics, though, that you put on the program because you have to talk about them. And if you don’t put them on the program, even though they’re not popular, then it doesn’t get picked up. And I remember putting sessions in the late 1990s, early 2000 about diversity that would have four people in a room, and individuals would say “there’s only four people in there.” And I’d say, “We’re still putting it on the program.” UDL was another one, anything on universal design, I can tell you I’d put in the smallest room in the conference, because it would be a great session, but it would draw like seven people. And one of the reasons it didn’t draw many people, and quite frankly, across all of higher education, people might love the concept, it’s really hard to do. If we’re going to do things like multiple means of expression, which is a key factor of universal design, it can’t just say that you can share your results in one way. What if you’re not good at putting text together, but you really have the idea down. So maybe you create a video, or maybe you have a play, or maybe you get in small group and have a discussion. Multiple means of demonstrating the information, so presentation, how do I explain this in a way that people can get it that’s always been hard, it is now being made really easy because we can go to AI with the correct prompts, and we can come up with multiple ways of expression, multiple ways of presentation. Lew was talking about having a class of 400 with the students in there. You could say to the students: “Here’s what I want to do. We’re teaching metacognition. Everybody think of something you love to do now, type into AI. I love to blank. And I want to learn more about metacognition so that I could explain it to my friend.” So I could type in I love long distance bicycling, and I want to learn metacognition. Rebecca might put in that she loves rock climbing. John might put in like he likes baking, and Lew might probably put in there he likes street fighting. [LAUGHTER] So you put those prompts in there, and then what pops out is everybody in the class gets to learn it in their own… I mean, what could be more universal than that? And so I’m just really excited about it. And we did the whole chapter, and Lew and I talked about it, and I think at the beginning I wasn’t even going to do a whole chapter, and he said, “Yeah, we need to do a whole chapter.” And so I think AI will open the gate so that people can finally do universal design in a way that’s meaningful without overwhelming them.

John: Faculty have been overwhelmed, first with COVID and now with AI, and many other things that are happening externally. How does your book address the reality that faculty don’t have enough time or energy to learn a whole new technology on top of everything else they’ve had to learn in the last several years.

Lew: Fair point. I think the thing we need to start with is the idea that we want to be careful and not this chase the technology, because if you do that’s going to make things worse, you’re going to get frustrated. What you want to do is try to use the tool to make things better. So I often talk about there’s 2, t-w-0, too many shiny objects out there. And one of the ideas with the too many shiny objects is there’s all these little prompts that people: try this, try that, try that, and that’s really frustrating for faculty, because it’s just a little bit beyond their skill set of what they want to do. It’s a biology example, and they have to bring it down into what they want to do. The other thing is, with the shiny object, is the idea of treating AI like this big shiny hammer, and then you’re running around trying to find all these curricular nails to try to beat down of how I’m going to use AI to do that, that is just such the wrong way to address AI, letting the technology lead first, you’re just going to make things worse and you’re going to stress yourself out even more. So what we want to do is we want to go back to basics. We want to think about backward design: at the end of this course, what should my students know? What should they understand? Then how am I going to assess whether they understand that? And then finally, how would I go about teaching that? And that’s where we decide whether AI might fit in or not. So again, it’s not the idea of trying to get AI in the classroom. I think as faculty members, we want to be really careful about how do we use it to be a creative assistant for us and kind of level up and improve our existing teaching practices. And the thing is, the book, all these teaching practices that we’re doing, these are well reasoned research techniques that have been laid out in chapters four through 10, backward design, ZPD, TILT, all the fun letters, UDL, all that stuff. But again, it’s trying to use that in a practical way, trying to develop your teaching skills more, and then just using AI as the tool to help you with that guidance along the way.

Todd: I wrote an article years ago. I was like, we just want to be well, that’s all we want. [LAUGHTER] So this concept of what we’re doing, if we think about what we do as faculty members out, as Lew pointed out, we’re chasing things a lot of times, and that’s just a problem. And this goes back to… remember when clickers came out, student response systems. Everybody’s like, “Oh, I want to use a student response system because my colleague’s using it,” and as faculty development directors, we are constantly trying to talk people out of using technology unless they actually needed it. Backward design. Why do you want to use it? Student response systems are always fun, because it’s like, I want to use student response systems and my friends and I would say, “Okay, so why?” “So we can gather student responses” and I’d say, “Okay, you know, you could just have them raise hands. Like, how many of you think this?” They say, “Well, yeah, but I want to know like anonymous.” I say, “Okay, have them close their eyes and raise their hands well.” “But I want to tally them.” “Alright, well have them close their eyes, raise their hands and then count them.” I mean, these things are not necessary. But then when they start to say, “Okay, but I want to know some qualitative aspects, what are they thinking and why?” Now we can use the technology. So I think that’s helpful. And so when you go back and look at what really takes time in teaching, those types of issues do, I can’t, like have everybody talk about muddiest point, because I don’t have time to read through those. Now you do, because AI can skim through those quickly for you. If you’re prep timed, you put in exhausted in there. I was exhausted because when I taught history and systems of psychology the first time, I would be up from 10 o’clock at night till two in the morning putting together a lecture. And I knew the stuff generally, but I had to go look for it. But I spent all my time looking. I was looking for the information. Then I’d find it, and then I would embed it. With AI I’m going to get the same kind of information, I’m just going to get it really faster, and because I can pull that together with the knowledge base I have, then I can put it out there. So prep time is going to drop, grading time is going to drop in terms of figuring out how to read through these types of things. Underpaid, by the way, I think you might have mentioned that one, just so we can’t do a lot about that. So sorry, yeah, for the underpaid part. But I want to finish with one quick thing here. There’s a book that I did actually get accepted for publication that I have never written. The contract was signed in 1994 and the reason I mentioned this is that what I wanted to do was take a stats book, but I thought we’re teaching statistics without kind of framework. So what if we took the old psych studies that were really the cool studies, took the data set, retrofit a data set basically that would give you the same data. Have students analyze the data, come up with what they’ve got, and then talk about what the data means, and then explain to them how that fits into like, that was the Bobo doll experiment, where Bandura had models hit a bobo doll, a blow up doll, and then they give the same doll to kids and see if the kids would do the same thing. You could replicate these studies while learning statistics. And I was told I couldn’t write that because most faculty members teaching statistics don’t know those studies. And so at the time, there was no way to do it. Now, with AI, you actually could, it would be very easy to say, I want to know more about Bandura’s Bobo doll study. And so I think that’s some changes that we’re going to have that are really going to be helpful.

Rebecca: One of the things that you’ve both underscored is leaning on the faculty member’s expertise because they can validate what’s coming through the AI tools. [LAUGHTER] So you’re not worried about the reliability, because you have that expertise, and the things that you’re suggesting people use are relying on that expertise to some extent.

Todd: Yeah, and that’s the thing. They’ve got the expertise. How can we help them with it? And help them by putting together a book that’s very modular. You can just go through and work through these things. Actually, the activities are set up, and Lew did this, I thought it was fantastic, they’re put in boxes. You could pull the whole thing out and make a workshop out of it. Very, very easy to pull this stuff out and do that, but by making these things very consistent, we know that reduces cognitive load, which is going to reduce some of that exhaustion and extra work.

Lew: And the other thing is, the examples were set up in a way, again, that they relate directly to what you’re doing in your classroom, your assignment, your example, not something that, again, the cognitive load that you have to translate from somebody else’s existing example. And again, that’s one of the neat things that AI allows you to do. Because now, instead of me saying in a book, do this example, I can put a little bracket there and say, insert your example from your discipline here, and all of a sudden you have individualized learning, individualized application for the teaching technique that you’re trying to do using the power of AI to do that.

John: And one of the things I’ve seen is that we have a lot of people attend workshops on AI just to learn how they can block it and how they can ignore it. But when the focus is on activities that actually allow them to do their work more productively or more easily or more efficiently, they get a bit more excited about it and then start thinking about how they could be using it to improve their courses and to improve their lives in general. And I think that’s a nice way of introducing people to AI who are initially somewhat resistant to it.

Lew: Yeah, it’s critical. I don’t think you can walk into a classroom and say, okay, students, we’re going to use AI today without understanding it yourself. And you hit the nail on the head there, the best way to do that is to do something that you are the expert in, and then learn to explore this technology on your own. It’s going to take a couple years for us to kind of get used to what’s going on. I think in a few years, we’ll be ready to think about how we might use this with students and things like that. But as a faculty writ large, we’re going to have to have a better understanding ourselves of just how to use those tools. And I think to your point about students misusing it and things, we can now have better conversations with them if we better understand how to use the tools ourselves for our own work.

Todd: And how people learn. [LAUGHTER] So we have tht published in the book too. That’s what it does. It helps you understand how these processes work, and so you learn about things like cognitive load. So then you can not teach cognitive load, but using that, build assignments that have lower cognitive load. That was one of the things that Lew came up… I love that one assignment that you came up with, Lew, is basically it’s you load the assignment in and says, “Where are students going to have high cognitive load in terms of really struggling with this assignment? And then AI says, “Oh, they’re going to stumble here, here and here.” And then you start rewriting it. It’s not a lot of extra work for you, and it helps the students. You don’t have to go through examples and have all those students lined up outside your door in office hours, saying, “I don’t understand why this thing works.” And so yeah, I think it’d fantastic.

Rebecca: I appreciate the idea of meeting people where they’re at, for sure. You mentioned something about a couple of years, maybe we’ll be ready to really integrate it with student learning. In five years, maybe, what does good teaching with AI look like?

Todd: This is kind of a fun one. I actually was asked to do a workshop recently that they were looking for a speaker, and they said they were interviewing different people, and they said, “We just want somebody come in and talk to us about what teaching will look like in 5 to 10 years.” I said, “If somebody tells you that they know what teaching would look like in 5 to 10 years, I would not ask them to give that presentation.” Because I don’t know, we used to talk five years out. Tom Angelo’s book with CATs was good for 20 years and still kind of worked. That stuff’s not going to handle anymore, because just in the last three to four years. Can you imagine, just before AI took off, go back just a few years and predict that it would look like this? So I don’t think we have a clue about what overall it’s going to look like. I do think we can look at some trends, though, and we can look at some directions we’re taking, because we can’t just throw our hands up and say “we don’t know,” but we want to be careful about what it’s going to be and it’s just going to be difficult, but there’s lots of good stuff coming out. And I’m going to actually ask Lew to do this, because earlier today he was explaining to me an article he’s reading. I think this stuff is fascinating that’s coming out.

Lew: Sure, I’ll jump in. So one of the things, not only do I keep an eye on higher ed and what’s going on with AI, but also the K through 12 space, and I follow a lot of folks on substack, it’s interesting. I think a lot of the discourse right now is going on in substack, as far as with AI and teaching and learning things. But anyway, there’s a fellow I’m going to try to pronounce his name, Nick Potkalitsky from Ohio, and he’s working with our Ohio Public Schools and writing about that. So the state of Ohio has mandated that by July 1, 2026 all school systems are supposed to have some kind of AI policy in place. So Nick is working with some of the larger school systems, Cincinnati, Columbus, Cleveland and things like that. And in this article he talked about on substack, he’s like, “Listen folks, this is going to be a three to five year rebuild. This is not going to be a quick policy fix. Rebuild means rethinking instruction, assessment, and curriculum, basically from the ground up. And how might we support teachers to make that happen and things like that. But teachers clearly need to be in that conversation. They need to be in that room.” This is the K through 12 space, and I think that’s only going to translate to higher ed as well. So the sooner faculty can get out there and start engaging in this technology and seeing what it works for them, then they will be able to kind of think down the road of how that’s going to affect their students in their classroom.

Todd: We have an epilogue in the book we did too after chapter 10, there’s a section in terms of what we think is coming along. And I just want to add one more quick thing here, when I think about what good teaching is, throughout my career… so I started teaching in 1984 so I’ve been teaching for a minute… and the concept here is that throughout all of that time, when I think about things I wanted to do, and what frustrated me were things that I couldn’t do because I didn’t have time or I didn’t have a way to do them. Universal design was just one example of that, but lots of things. With 400 students, I would love to ask them an essay question, but there was no way to look at that, even to get a sense of what they were writing. I would like to put students in small groups and looked at multiple versions of what they were talking about. There’s so many things out there. And now, as I start looking at what the future of teaching might look like, it’s kind of like one after another we just remove those barriers. So I don’t know what it will look like, but I know it will look better because it is removing things that we can’t do. And I will just end with my chunk of saying this is that we so quickly, when this came out, went right to cheating. Just immediately, the first articles out were, how are we going to stop students from cheating? How are we going to stop them? Students were cheating before AI came along. Don McCabe did a lot of work on this, and the proportion of students cheating in some way was off the chart. It made it easier, no doubt about that, and it does create a real problem, because if students can just jump in and do AI, then there’re many that will do that. But I don’t think we have to go there. And Lew and I talked a lot about this too, is that we shouldn’t just go there. We should start looking at opportunities. And I just talked to someone the other day who said, like, “How are we going to know when we can fully trust AI when it gives us an answer?” And that’s the kind of stuff that just drives me nuts, because when we talked about hallucinations, and so many faculty immediately say, “Oh, I put that in and it made a mistake.” So do I, sometimes. There are times when I make mistakes. I’ve heard my colleagues say stuff and I say, “That’s not true.” And so it’s no different than humans in that respect. We’re going to be able to trust it when we can look at it and we know it’s right, but it is getting better all the time. And Lew said it earlier in this discussion we’ve had here today too, is we can’t focus on what it was. We have to focus on what it can be. And I think looking forward for teaching, there’s some really cool stuff coming.

Rebecca: I think one of the things that you’re highlighting, both in your book and how you responded, is it remains anchored in the science of learning and just how we’re using the technology, and things might shift, but if we keep focusing on good teaching and learning practices [LAUGHTER] and just get them better, it’s not a surprise.

Lew: Well, it’s interesting, especially at the higher ed. We’re all trained in a very specific discipline, a very specific area. We’re experts in that field. We were never trained to teach, or very few of us maybe had that one course as we were TAs or something like that. And again, what the AI is allowing us to do is to further our expertise and our teaching and to kind of push the boundaries where we just didn’t have that training before. So I think, as Todd pointed out, I think there’s going to be a lot of opportunities just to make things better, hopefully.

Todd: Before we wrap up this question too, I’m just going to kind of put a charge out there, because Lew mentioned a little bit earlier, but in the model we have for adapt and stuff, for those of you who are listening, if you haven’t done it yet, just try dumping one of your assignments in for a topic or an area that you know students struggle with a little bit put into the AI. And the book’s got some examples on how to do this, but you could just say, “Where will the students struggle with this?” When I teach statistics, distribution of sample means, always give them fits. It took me years to figure out how to get around that, but if I was a brand new teacher and never taught before, I could put distributional sample means in there and say, Where will my students struggle? And it will tell you, here’s some places. And then, if you want to set that off to the side and then teach it and see where they struggled and make a comparison. And if it kind of lines up a little bit, you know it’s a gateway to kind of figuring out where the problems are going to be before they emerge. Wouldn’t that be cool?

John: So this is the first time you’ve worked together on a project like this. Could you talk a little bit about the working relationship and how that evolved?

Todd: Yeah, let me jump in there real quickly. Number one is writing a book is hard. I mean, it is really, really hard. And AI actually has helped us out with this too. So I wrote a book, this New Science of Learning first edition took two and a half years to write that thing. This book was nowhere near that, six, seven months, and we put a lot of time and energy into it. But the point is, it’s hard. Working with somebody is even harder because now you got to talk about what you’re doing and look at different points of view and everything else. And I think what was really cool with the way Lew and I kind of set it up is that we were coming from different directions. I had the learning theory. He was coming in from the AI, I would write a bunch of stuff, and he’d say, that’s really cool about all that metacognition. Maybe we can change it from like 3000 words down to, I think, 42, one time. And I said, I’m not explaining metacognition in 42 words. And then he gave me like 150 so we had to compromise and go through that. I would just say, working with him, he gets stuff done fast. He is really, really good at things in terms of understanding AI component. His prompting was amazing. I don’t know he’s a little mathematician-ey for me sometimes, but, you know, it was alright working with him. So all in all it was a positive experience. And I think Lew would say the same thing. So what’s the next question? [LAUGHTER]

Lew: No, it was a lot of fun. I really appreciated, as you’ve already mentioned, Todd is a very prolific author, so he was able to help us navigate all of those rough and challenging waters. So I was so much appreciative. But I think we complemented each other well, as far as what we brought to the table, and it’s exactly you know, what the book needed. And in particular, I remember Todd that GLCA, the Great Lakes College Association talk that you gave, what was the title of that? You remember something about neurodiverse learners?

Todd: It was basically how AI can help neurodiverse learners to be better learners. The title is way better than that.

Lew: Right, and Todd, he graciously used himself as an example, I will say. So a lot of things made sense to me [LAUGHTER] after that presentation Todd, [LAUGHTER] I’ll just leave it at that.

Todd: So, let me just say this. Yeah, I explained a little bit about ADHD and some of the characteristics of people who have ADHD, like time blindness, where you think something’s gonna take 40 minutes, but you don’t have a clue? Might be 10 minutes. Might be 17 hours. Who knows. You set up your task list, but you’ve never, ever finished a to-do list. I have never finished to-do list. And yeah, I went through all of those types of things and a couple of other things that I kind of struggle with. And then Lew when we finished, he says, it’s been great working with you, but I’m not sure if I’d heard that presentation first I would have signed the contract. [LAUGHTER] Well, you did it.

Lew: No, it was fine. It was fine.

Rebecca: So Todd, some of our long-term listeners might be curious what role opera played in this book. Didn’t you listen to a lot of opera last time?

Todd: Yeah, you know what? Let’s just throw this in there real quickly, because this is part of the ADH stuff that I’m learning, to whatever extent this fits in with this whole interview here. So ADHD is one of those things that, much like dealing with gluten intolerance and several other things, people think, if you’ve really got it, you must have known it your whole life. There are people who find out later in life that they have this thing. I always knew that kind of general things with ADHD which always struggled with a little bit. And I knew I had some of it. But, I mean, I was 60 years old when I was diagnosed, and so that concept there is suddenly I started to realize and read bunch of books about things like time blindness and some other things, but I had a terrible time writing. And a person, Tamara Rosier, who wrote a fantastic book, Your Brain’s Not Broken. It’s been reviewed in the New York Times and stuff, she said, “listen to opera, like, crank it up. Don’t do ear damage, but crank it up and do that while you’re writing.” And I said, “that will never work, because I have to have it quiet.” And she said, “Just try it.” And I put opera on and cranked it up, and I churned out work like I’d never turned it out in my life. And she pointed out to me that my brain’s always looking for stuff. Part of it would settle on that, and then it just kind of left this focus for the rest. Still had to have absolute quiet when I read. But for writing, that worked. I got put on some ADHD medicine, and two days after I started the medicine, I cannot listen to anything while I write. [LAUGHTER] To me, that’s fascinating, because what it now means is my brain’s actually kind of slowed down enough that that becomes a distraction. But that to me as a cognitive person, that differentiation is fascinating.

Rebecca: That is fascinating,

John: And that’s a nice preview of our next podcast that will be coming out next week. We’re talking to Karen Costa about ADHD and her experiences with that.

Todd: She’s really good.

Rebecca: So we always wrap up by asking, what’s next?

Lew: So I’m a mathematician by training, and I was just at a recent large gathering of many of our professional organizations, and for years now, they’ve been calling for, especially in like the high school setting, that we need to teach more statistics, data analytics, modeling, in other words, creating mathematical pathways, kind of beyond the calculus curriculum. The rub is, is that the way folks are trained, they’re basically trained for the calculus curriculum and not these other things. So what I’m doing is, I’m actually working with some organizations like Math for America to consider how we might take frameworks to increase the number of teachers that were able to develop these courses in these different areas. So what we’re doing is we’re looking at something I like to call AI-assisted retooling, so teachers are learning new content alongside an AI partner to expand their mathematical pathways. So, that’s kind of a really exciting thing that I’m starting to work with. We got a few case examples that we’ve been doing. I’ve been a case study myself of doing that. So, I’m really excited about that. And of course, you know, I’m continuing to work with teaching centers on workshops and things around the book.

Todd: Nice. For me, so I got another book started, because why not? These are always fun. This particular one’s going to be different, though. So I’ve probably said about as much as I can in terms of some of the teaching tips and kinds of things we do. I really think that what’s going to happen pretty soon is there’s so much stuff coming out with AI, real life stuff is going to become something I think people are going to cherish. So the project I’m working on right now is looking more at how I’ve taught in different aspects of it, but what it really is going to boil down to are things that I experienced in the classroom. They’re really kind of unique, interesting things, and then how it played out and how it could be different. And so I had a student one time have a grand mal seizure in my class. And the quick example here is I quickly ran down to a nursing station. It was just around the corner. I ran over there, got somebody, brought them back, and the nurse came back, and we came into the classroom. And I, right now, would tell you… that was my first year of teaching that happened… I would never leave a classroom when an emergency is going on. I should have said to somebody, “Hey, go to the nursing station.” So what I want to do is talk a little bit about what I’ve done, some of the ways I’ve done it, how I would have done it differently, and then how that ties into learning and different aspects. So I really want to make more of a personal story. I don’t know if anybody will be interested in it, but, you know, we’ll give it a try.

Rebecca: Sounds like an interesting book. I’d read it.

Todd: Good. I just sold one. Oooh.

Rebecca: Yeah. [LAUGHTER]

John: Maybe two. [LAUGHTER]

Rebecca: Yeah, maybe two.

Todd: No worries, but thank you, appreciate that.

Rebecca: People have anxiety about, like, what if X, Y and Z happens in my class, and hearing personal stories about how those sorts of things have been addressed and strategies for addressing them, I think eases some worry for folks, so it could fill a gap.

Todd: Yeah, I appreciate you saying that, and I think one of the big ones is that it’s been about 40 years of teaching, and the 40 years of teaching, I think the framework I’m gonna go with, I’ve made almost every mistake you can make, and I still am seen as kind of a person who’s written these books. I do know a lot about teaching, and I’ve learned a lot about it, but you don’t become a better teacher without making mistakes along the way. I don’t think anybody can. And so I’ve done that. And I probably should point out, too, as Lew said, I run around some campuses. I love meeting people and going to campuses. I’ll still keep doing that.

Lew: So Todd, three years ago, I did a column for the MAA, it’s the Mathematical Association America. It’s called Still Learning From My Misstakes. And mistakes was spelled with two S’s, anyway, but yeah, exactly that, that idea of, I think, showing your vulnerability, especially at the part in the career that he’s at, like, oh, Todd Zakrajsek does everything perfect. I think peeling back the curtain and showing the mistakes that we made for the folks who are coming up, that is so refreshing to see that and what we did well and what we didn’t do well, and then kind of examples of moving that forward. So yeah, maybe three books, you might sell three. [LAUGHTER]

Rebecca: Definitely worth the investment now. [LAUGHTER]

Todd: Lew, that will cover my next tea. [LAUGHTER] But thank you, appreciate that.

Rebecca: Well, thank you both for joining us and for sharing your work.

Lew: Thank you so much. Appreciate it. It was fun.

Todd: Appreciate the opportunity.

John: Well, thank you.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

[MUSIC]

427. Open Exchanges in College Classrooms

Ideally, college classrooms provide students with a comfortable but challenging environment in which diverse ideas and viewpoints are openly exchanged; the reality they experience, though, is often quite different. In this episode, David Laibson joins us to discuss how Harvard University is attempting to identify and address barriers to this ideal.

David is the Robert I. Goldman Professor of Economics and a Faculty Dean of Lowell House. He has published dozens of heavily cited articles on a wide range of topics, including behavioral economics, self-regulation, behavior change, household finance, and aging. David is a Research Associate in the Aging, Asset Pricing, and Economic Fluctuations Working Group at the National Bureau of Economic Research, member of the National Bureau of Economic Research, and serves on numerous advisory boards. He has received Harvard’s Phi Beta Kappa award and a Harvard College Professorship in recognition of his high quality teaching. David is also a co-author of popular textbooks on introductory economics and  a co-editor of the Handbook of Behavioral Economics.

Show Notes

Transcript

John: Ideally, college classrooms provide students with a comfortable but challenging environment in which diverse ideas and viewpoints are openly exchanged; the reality they experience, though, is often quite different. In this episode, we discuss how one institution is attempting to identify and address barriers to this ideal.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

John: Our guest today is David Laibson. David is the Robert I. Goldman Professor of Economics and a Faculty Dean of Lowell House. He has published dozens of heavily cited articles on a wide range of topics, including behavioral economics, self-regulation, behavior change, household finance, and aging. David is a Research Associate in the Aging, Asset Pricing, and Economic Fluctuations Working Group at the National Bureau of Economic Research, member of the National Bureau of Economic Research, and serves on numerous advisory boards. He has received Harvard’s Phi Beta Kappa award and a Harvard College Professorship in recognition of his high quality teaching. David is also a co-author of popular textbooks on introductory economics and a co-editor of the Handbook of Behavioral Economics. It’s really an impressive list of things there. Welcome David.

David: Thank you. Glad to be here.

Rebecca: Today’s teas are: David, are you drinking tea with us today?

David: Well, I don’t actually have my tea cup with me right now, but I do have my preferred tea on hand [LAUGHTER] because I knew you would ask. So my favorite tea right now is a Korean matcha called Osulloc, and it comes in a very handy format where I can basically take my matcha tea bag, drop it in my water and have my lovely cup of tea, but I don’t honestly have one right now. [LAUGHTER] So, I thought you were gonna ask about what I like to drink, as opposed to what I’m drinking.

John: And I am drinking a Twinings oolong tea today.

Rebecca: Oh, nice. Must be the end of the semester is getting a little intense with the black teas, huh?

John: It is. [LAUGHTER] I think this is my third or fourth cup today. [LAUGHTER]

Rebecca: I have a decaf Irish breakfast this afternoon.

John: That’s the difference being in the administration. [LAUGHTER]

Rebecca: I have about a week, have about a week. [LAUGHTER]

David: Well, good luck reaching the endline.

John: So, we’ve invited you here today to discuss your work as co-chair of the Classroom Social Compact Committee, which we had read about in an October 6 article in The New York Times. Can you tell us a little bit about this committee?

David: Sure, the committee did most of its work in 2024 and issued its findings report in 2025, early in the year. Maya Jasanoff, another faculty member here at Harvard, is the co-chair of the committee, and our report was both a description of the state of affairs here at Harvard and a set of recommendations for addressing many of the problems that we uncovered or documented. The committee was charged by the Faculty of Arts and Sciences Dean, Hopi Hoekstra, and the charge, I won’t read the whole thing, but it included describing the nature and purpose of the faculty of arts and science classroom and developing practical recommendations for engendering a vibrant learning environment in the classroom.

Rebecca: Can you talk a little bit about some of the primary issues that were raised in the report?

David: So, there’s a long standing recognition that the classrooms here have a aspirational role to play, that perhaps we’re falling short on on many dimensions, and let me review some of them that were discussed and documented in the course of writing our report. So, one thing that we’re concerned about is the degree to which students come to the classroom not completely comfortable sharing what they think when they raise their hand, if they raise their hand. There’s a fair degree of self-censorship, an extent to which people, for various reasons, may not feel comfortable speaking up. It may be that they’re shy and don’t want to say something that might be wrong or that they don’t want to say something that may be viewed critically by other students. And so one aspect of the classroom that is problematic is the degree to which people are not comfortable bringing authentic voices into the classroom setting. Another significant issue is the degree to which our students have, in some cases, focused energies outside the classroom, to the extent that it actually undermines the academic center of their lives here. Now I don’t want to imply that that’s the fault of our students. I think in many cases, that’s a reality that our faculty are not creating classroom experiences that are sufficiently stimulating and therefore engaging our students’ interest. And so I think the un-centering of the classroom, which has been occurring for decades here, is a two-sided phenomenon. On the one hand, it’s students that are focused elsewhere. On the other hand, it’s faculty that may not be creating the most stimulating and exciting environment intellectually in the classroom. And I think we’ve all agreed, unfortunately, that that’s okay, and this report is part of reversing that consensus and reasserting the principles that I think we would agree with, but haven’t always been our practice here at Harvard.

John: And I think what you’re describing is something that we’ve seen at pretty much all institutions, to some extent. But one of the things that really impressed me about the report was the extent to which you gathered data and the ways in which you gathered data, because that could be a useful model for other institutions as well. Could you talk a little bit about what types of data you gathered and the process of assembling that?

David: So we did a year of data gathering, and as you point out, it was pretty aggressive and extensive. We wanted to make sure that we really had our finger on the pulse of the many different communities and the many different stakeholders, so we held well over 30 group listening sessions, and then I would say, hundreds of one-on-one listening sessions, where you sit down with a student and have a one- hour lunch and get a full download from them in the most confidential setting, where it’s just a one-on-one conversation, as opposed to someone volunteering an opinion with 40 other people in the room, which can be a little daunting. We also consulted with colleagues and consulted with many different committees that sit here in the Faculty of Arts and Sciences that oversee our curriculum. So the Educational Policy Committee, the Graduate Policy Committee, the General Education Committee, a place called The Bok center, that is a sort of mentorship for teachers unit, and we talked with department chairs. We talked with divisional deans. So we really went out there and gathered a lot of data. In addition, we looked at 11 different surveys that were fielded either by us or by other groups inside Harvard, measuring undergraduate opinions, graduate opinions, faculty opinions, alumni opinions, and that is the maximally confidential space for people to give answers, because a lot of these surveys are done without any ability to identify the respondent other than knowing that they are, in fact, a Harvard affiliate, because they got into the survey using a Harvard ID. So we looked at five undergraduate surveys, three graduate student surveys, one faculty survey and two alumni surveys, and by that point, we had a lot of data that would enable us to, I think, have a fairly precise understanding of at least what the community thought and what they were willing to share with us in these different formats.

Rebecca: So, you mentioned the self-censorship issue that bubbled up across all of these different audiences that you surveyed and collected data from. What are some of the consequences in terms of student intellectual development?

David: So there’s a lot of adverse consequences. One is the obvious problem that students don’t feel, in many cases, that they belong, because their perspective is not welcome, and that, I think, undermines their ability to thrive. They’re also not developing the critical skill of learning how to speak publicly. If they’re constantly suppressing their voice in these public settings, and the classroom setting, then that’s a skill that isn’t being nurtured. I think perhaps one of the most important problems with a classroom where people don’t feel that they can say what they really think is that there’s this false impression of consensus when in fact there are enormously divergent viewpoints. They’re just invisible because most people that are speaking are speaking in a relatively narrow domain with respect to maybe the most sensitive issues. So when you have a classroom that is a place of self-censorship, and I want to emphasize many classrooms don’t have that problem. But when you have a classroom where people are self- censoring, you get a false impression of homogeneity. You get a set of skills that are not being developed. You get many students feeling that they’re just there on a performative basis, and it undermines the whole intellectual enterprise. We’re here to learn and really compare ideas while all that’s out the window when you have the impression, and again, this is only in some classrooms, that there’s a kind of acceptable viewpoint, and anything that sits outside that viewpoint you better keep quiet about. But I want to emphasize, a lot of people think that the way the law is laid down is through faculty and graduate student teaching fellows. Indeed, the students told us that mostly they were concerned about what their fellow students thought. So, if anyone has the impression [LAUGHTER] that this is a sort of monoculture problem that is driven by or primarily by instructors, I want to, right from the start, clarify that overwhelmingly, the students said that in many cases, it was fellow students and their judgments and what would happen after class in the dining halls that they were most concerned about with respect to saying something that might be unpopular in class, and not the bad grade they might get. Indeed, I was quite happy to hear that the faculty were the people that the students were the least concerned about. So I think there’s more concern about some of the graduate students that are grading their papers in a big class, but especially there’s concern about their fellow students. Now that’s not to say that the faculty don’t have any role to play in changing this equilibrium. Definitely, the faculty need to change norms and need to lead with their voice this new approach to a classroom where more perspectives are sought out and encouraged and welcomed, but it is, I think, important to emphasize that it’s not just the faculty that are contributing to self- censorship, it is fear of what other students are going to say in the dining hall after class.

John: One of the more striking statistics there was that in one of the surveys, 33% of Harvard’s college students reported that they felt free to express personal feelings and belief, as you’ve noted, a lot of students are self-censoring. However, there were some other statistics based on individual course evaluations that suggested a much lower rate. How was that explained in the report, that discrepancy between the reports from seniors and the individual course summaries.

David: Yeah. So just to highlight the tension, if you ask people about their overall academic experience, only a third say they were comfortable speaking openly, but when you ask them about the individual course that they just took, so ask them about one course, and you get about 90% saying that they’re comfortable speaking openly. So clearly, what we’re learning here is that this is not a problem that people are experiencing everywhere, all the time. It’s a problem they’re experiencing in some places. So when you ask people, as they’re leaving after four years, what was it like? Did you feel the need to self-censor? They say, “Yeah, sometimes.” But when you ask them about the class they took last semester on 19th century history of Brazil, they say, “No, actually, in that class, I did not feel the need to self-censor.” So, the distinction, I think that’s important to highlight, is that it’s not a problem that’s occurring all the time for our students. It’s a problem that’s occurring frequently enough that it’s a real concern, but not so frequently that it’s affecting every curricular experience. And I think that speaks to a couple issues. First, the problem is somewhat contained. But secondly, there probably is a degree of intellectual self- segregation that is contributing to the sense of, “Well, it’s not a problem in my class.” Sometimes I ask students, “Well, what happened in your class? What was the curriculum?” And they’ll describe a relatively homogeneous student body in that class. And then I’m not surprised that if the classes has advertised itself as having a particular viewpoint, and the students get that message early on, and the students that want that viewpoint stay, and the students that don’t want that viewpoint move on, you can easily see how people might say, “Yeah, in that class that I took, I had no trouble saying what was on my mind.” But then one realizes that in parentheses, that’s because the class ended up attracting a relatively homogeneous set of perspectives in the student body. And so I do think all of these considerations make it challenging to know exactly how deep the problem is. I definitely think we can over-exaggerate it if we just cite the 33% number of people that feel comfortable speaking their mind. But I think we can also kind of over minimize it [LAUGHTER] by saying if you look at individual courses, 90% of the students say that in the particular course they are evaluating right now, they feel free to speak their mind. We don’t know how much of that is a kind of self- segregation into curricular environments that they feel comfortable in.

Rebecca: What are some strategies, maybe both structural or also within a class, to work against this?

David: Well, I think you start with awareness as an instructor, I think instructors don’t realize the degree to which their students feel pressure from other students to accept a kind of student-endorsed viewpoint. And I think faculty are trying very hard to have real conversations in their classrooms, and are often working to prevent their own viewpoint from becoming, in some sense, the only acceptable viewpoint. They’re not necessarily jumping out on day one and saying, “This is what I think,” they’re creating space for student perspectives. They’re working hard on that front, but they don’t realize that it’s not just that students are trying to glom on to what the faculty member thinks, it’s what the other students think that is a real issue. And so I think faculty need to be explicit in saying, “Hey, in this classroom environment, we really encourage diverse perspectives, and I’m going to be thoughtful in drawing them out.” And so I’ll give you examples of how we do that in my class. You probably have both have the experience of that wonderful student who is always quick to get their hand up right away, and if we always call the students who get their hands up in the first five seconds when we ask a question, or when we open it up for questions, we tend to get a relatively homogeneous set of answers because it’s the same students that we’re privileging. They’re the students who get their hands up the quickest. And if we call them, once there are a few hands up, well, we’re going to over sample the same students. And so one thing that we do in my classes, we, by that, I mean me and my co instructors… we try to let the question settle and not pick someone immediately. And I have something called the 10-second rule, where once I ask a question, I’ll count to 10 in my head, quietly, and only then, when I’ve allowed 10 seconds to pass, do I finally start picking from the hands that are up. And at that point there’s a lot of hands up, and I have my pick of students, and I can go for students that have not been contributing so far in that lecture, or so far, even in that semester. So I love the ability to bring in this far more diverse set of student voices by just giving them time to get a question together. And I think there’s lots of ways that faculty can launch a more diverse conversation by not allowing a small set of voices to become the dominant voices, and by looking for opinions that lean a different way than the first opinion you got. So you can ask a question, and some student says, “Well, I think A,” and you can ask, “Well, who thinks not A?” And then you can draw in the range of opinions, rather than allowing the first student to say, “I think A” and then the second student to say, “I think A too,” and then the third student “I think A three,” and everyone thinks a. And that’s pretty much the herding effect you don’t want. And you can be very intentional in creating an anti-herding effect by only asking for questions or perspectives that create the heterogeneity that you’re trying to engender, rather than allowing students to herd on the first answer that comes out of the room. So I think there’s a range of strategies, and they had to be launched on day one to get the equilibrium right in the classroom. Students need to hear the message early on and hear it repeatedly, that this is a place where we’re looking to hear from different perspectives, and we’re not trying to all agree on something. I am very disappointed when I hear [LAUGHTER] faculty telling me about how much agreement there is in their classroom and how excited they are by that. I think when everyone’s agreeing on something, it’s probably a mundane thing, or they’re observing false agreement. There’s enormous disagreement among our students, and if you know how to draw it out, you’ll get it. And if you get everyone to agree together that something is true, well, I ask you, what does everyone agree on that isn’t something that’s utterly trivial.

John: One of the things I’ve done in my classes is I do a lot of polling, but also I have a Catchbox, one of those little cubes with a microphone in it. And when I’m asking questions where there is likely to get a lot of diversity, I’ll wander up to the back of the room because that’s where the students are most likely to sit who are least likely to volunteer and either hand it to them or toss it down the aisle a bit, because the same students always sit up front with their hands raised all the time, and students in the back, at least in my class, are much less likely to ever raise their hand, no matter how many other people have done so. I think one of the things you recommend perhaps using the Bok Center to have people work on alternative teaching strategies to draw out some of these things.

David: Yeah, we do some of the things that you just described. We also sometimes kind of insist on questions from the back of the room. And we also have something called a pre-lecture activity where we get everyone to anonymously answer polls or play games or do something that we’ve created before the lecture. And it’s amazing how much variation you get on these pre- lecture activities. And then everyone in class is astounded when we show them the histograms of responses, and they realize, “Wow, I thought everyone agreed with me and now I realize half the class has a totally different viewpoint.” And it’s not just now a hand or two, they’re seeing data that represents the entire distribution of people in the class, and everyone says that’s the thing they remember the most from the semester, seeing their own data and realizing how different their own perspective is from that of everyone else in the class. And it’s an eye opener.

Rebecca: One of the other things you mentioned earlier was the self selection into classes. Do you have some strategies to encourage folks to explore more things? [LAUGHTER]

David: Yeah, so certainly that’s advice that I give all my students. I’m not sure that that’s [LAUGHTER] the best way to get that advice to be accepted, just sharing it. I certainly take the view that if you are taking a class that’s just going to tell you what you already believe about the world, you’re less likely to be learning than by taking a class that might challenge your preconceptions. That said, I think one thing that instructors should do, in my view… of course, they have academic freedom, so they’re free to decide whether they want to do this… but I would encourage it as good pedagogy, is to offer a range of perspectives in a class and to, themselves, offer the alternative view on an issue, as opposed to the view that is the maybe main thrust of the course. Indeed, I do this myself, I’ll give students the range of evidence on an issue, and I’ll say some scholars think this, and here’s why they think this, and some scholars think this, and here’s why they think this, and here’s the body of evidence that we have, and you can see all the different empirical estimates that have been generated in scholarship in the last 15 years, and so we’re going to be very candid with you about the limitations of what we know and how scholarship has yielded different answers, rather than picking the study that I most like, that is most aligned with my personal viewpoint and teaching it that way. I think good pedagogy goes hand in hand with acknowledging the range of viewpoints that exist in the scholarly community and being forthright that there isn’t a single viewpoint that happens to match my own that is the right approach. And we even have a lecture in the introductory economics class on epistemics, where we talk about the limits of our knowledge and all the ways in which our own views have evolved as we’ve professionally been exposed to more data, seen new research, changed our minds, and so we try to model for our students this concept that there isn’t a set of truths carved on stone that we’re going to hand them. And I would recommend to colleagues that they teach their classes that way. And I think students should also think about learning that way. If they think that they have already discovered the truths carved in stone at age 19, [LAUGHTER] well, they’re in for a big surprise about how the world works. They’re never going to find those truths carved in stone, and they certainly don’t have them already at age 19. So I think we need to message that more in our classrooms.

John: One of the things you’ve mentioned is that students don’t always feel free to express their opinions, but there was also in the report a discussion of student motivation, student engagement, and the relative emphasis that students put on the classes versus the social life and extramural activities. In the report, there’s a number of recommendations, one of which you’ve already discussed in terms of changing how we teach in the classroom. But what are some of the other recommendations of the committee relating to these two issues in terms of getting more students engaged in class and putting more of an emphasis on class, as well as perhaps being more free to express their thoughts in class.

David: I don’t want to create too strong an impression of certitude here we are saying in the report that recentering the classroom, recentering academics, is an important goal going forward, and indeed, we’re already making progress this year where I’m seeing tangible changes in this current academic year, which is really giving me a lot of encouragement That said, the extracurricular life is rich and serves many purposes. So we’re not trying to convey the message that all of that should be shunted aside and people should focus exclusively on their academic lives. I think it’s about finding the right balance, and for some students they’re already well balanced, and for other students, maybe they’re unbalanced with a search for courses that ask very little of them. They’re called gems here at Harvard, g-e-m, and that means low work, high grade, and so maybe not so many gems should be offered, and maybe students shouldn’t seek out a course that is called a gem, and if people feel that they’re so busy out of class extracurricularly and they’re searching for gems, that that’s a sign that maybe the balance hasn’t quite been found for that student. So I think that we should, as a community, ask ourselves, “Well, what courses are famous for having a low lift of work and a very high grade distribution?” and then sort of ask, “Is that the sort of course that we want to be offering?” And that means that for that kind of curriculum, maybe we ask more of our students. Maybe the readings are more demanding, maybe the grading is tougher. So I think that there’s some suggestion to the community of instructors that we might think about raising our standards. And there, we’re thinking about what we ask our students to do out of class, what we ask our students to do even in class. So there’s the cell phone that’s inevitably taking the student’s attention away from what’s happening in the classroom and then 75 minutes have gone by, and you ask the student as they’re leaving the room about what just happened, and because they were parallel processing through the whole thing, they actually didn’t get very much out of it, and 75 minutes were spent kind of scrolling some social media app, and the student might think they got a lot out of that 75 minutes, but in fact, it’s very hard to do two things mentally at the same time. So I think we need to create more stimulating classrooms, more demanding classrooms with respect to assignments, we need to not create a classroom where there are so many A’s that the student can essentially only distinguish themselves by joining the consulting club outside of class and putting all their energy into that. So I think that there’s a lot to be done by instructors in creating a more exciting classroom, a classroom where excellence is acknowledged with higher grades, and it’s not that the norm is an A. I think students might also ask themselves, is the best use of their four years here to focus on things that are happening outside of our classes rather than inside our classes. And if the answer from the students is, well, actually, it’s better outside the class, then I think the faculty need to double down on making the in-classroom experience better. So a lot for us to think about as teachers, and our obligation to create an exciting curriculum that makes students thrilled to come to class. That should really happen. I mean, students should not come to class grudgingly. They should come to class with excitement because something interesting is going to happen, because there’ll be a conversation that will be memorable, because they’ll have an insight that they’ll take with them for the rest of their lives. I mean, classrooms are some of the most remarkable, extraordinary experiences one can have in a lifetime. And if it’s not that, then it’s on us faculty to up our game and create these experiences that are so memorable, that are so transformative, that are so important in the development of our students, that students are actually eager to be there and are not going only because attendance is being taken. Though I might take attendance if that’s a problem too, but for sure, the goal is a classroom that is a wondrous experience, that is the highlight of one’s life, that is sad as you leave it behind and graduate from college. Graduating from college should be, on one part, an exciting moment to move on with your life, and on the other hand, a very sad moment when you’re leaving behind this incredible opportunity to sample intellectually four courses that are life changing every semester. Well, let’s create those courses together.

Rebecca: I think a life changing course, seems like some faculty might have some imposter syndrome [LAUGHTER] around being able to achieve that. Can you talk a little bit more about some of the strategies in the class to make that engaging experience where students maybe don’t want to interact with their phones and they want to fully participate in class.

David: Well, I think students, by and large, do want to participate in class. I think the phone is sort of like a drug and everyone says, “Well, I don’t want to be a heroin addict in five years. I’m going to quit between now and then.” And of course, it’s very hard to quit the addiction. I think phones have a lot of those addictive properties. You enter the classroom thinking, “Gosh, I really need to focus.” I mean, this is my own experience as a student. There weren’t cell phones, but there were plenty of things that one could let one’s mind wander to. So I always enter the classroom intending to take good notes and really focus and not get distracted and learn intensively, and often I found myself drifting off. So we’re all easily distracted, and the cell phone is easy distraction on steroids. So I think students want to learn, and I think we need to create environments that facilitate that learning and make it easy. There’s so many classes that I’ve attended where literally, I was on the edge of my seat through the whole thing and excited because there was something important being said, some conversation with students, among students, between students and faculty, that kept me engaged. And I think students want that. I don’t think they are eager to spend these four years blowing off their courses. I think that is something that happens as a unintended consequence of all the other things they’re doing, like the conversation with a roommate that starts at 11pm and doesn’t end till 3am and after four hours of debating every single philosophical issue under the sun, you finally go to bed, and then, for your 9 am class, you’re very sleepy, and you get to the 9 am class having not gotten enough sleep, and you’re barely awake. That student was excited to be debating philosophy at two o’clock in the morning, and unfortunately, got to class in a drowsy state because of it. They’re not intellectually unengaged, but they’re still not going to really thrive in that classroom because of the sleeping that they didn’t get. So my sense is that students want to learn, and we just need to help create environments where it’s conducive to learning, where being in class isn’t going to put you to sleep, it’s actually going to wake you up, because so many exciting things are happening with respect to what is being said, debates that are taking place in real time, opportunities to ask questions, moments where we’re all on the edge of our seat because a student is actually pushing back on a faculty member on some issue and there’s a interesting dialog happening, maybe students also interacting with each other on some issue that is being discussed in the class, and so there’s a conversation, there’s issues that are surprises, there’s intellectual turns that were unexpected, there’s data that’s surprising, there’s ideas that are engaging. I mean, these are all aligned with what our students want. And I think the problem is often they come to class, and actually it’s a pretty boring lecture, and that’s not their mistake, that’s ours.

John: And along the lines of mobile devices, one of the things recommended in the policy is that mobile devices be either banned or discouraged, except in the cases of documented disabilities or when they’re being used actively for some component to the class. And in your class, the article mentioned that you had a device- free zone, but many people chose not to sit there. How can we encourage students, perhaps, to not use mobile devices?

David: I want to be clear, the policy in our class is no cell phones anywhere, so that’s a universal ban across the entire classroom, but we do allow students to use laptops and to use iPads if they want to. Though we also have a iPad and laptop free zone, I struggle with the question of laptop and iPad use, because for many students, they, I think, legitimately view it as an aid for learning, just a regular academic experience, I like to take my notes on an iPad, for example, as opposed to any need to use an iPad. So, I’m open to students doing that. I do warn them that it’s a slippery slope. Once you have the laptop open, you’re one click away from all the distractions and temptations of the internet, and that will really undermine your teaching. And what we do in our class is we actually hand out photocopies of the slides so they can commit to keep their laptop closed by simply using our photocopies and taking notes with a pen on our photocopies. And most of our students do that. That said, I think there are students that want the hybrid experience, and probably some who are taking advantage of being in a laptop allowed zone or iPad allowed zone to occasionally check out and check into their email or everything else that’s happening. So I’m torn about exactly how to handle the classroom environment. I’m open to professors that are going to do a complete ban. I think that’s fine. Obviously there’s accommodations, and we, I think, need to be mindful of that, but a complete ban outside of accommodations. But I’m also open to saying, “Hey, these young people need to learn how to develop this skill of living in a distracting world, and maybe part of developing that skill is realizing that after 75 minutes in the classroom, they have gotten nothing out of a lecture because they found themselves doing one thing after another on their laptop.” And so there is an upside to the distraction, which is a little bit of growing self awareness, which emerges as people begin to discover that we think pretty serially and cannot think consciously on parallel streams, and consequently that skill set of learning how to operate in this distracting world is developed in a room that allows them to use these electronic devices, though I think the cell phone is just so obviously destructive that I’m happy to nix that.

Rebecca: The policies that completely ban all devices except for accommodations… are we kind of skirting or dancing around issues of privacy when it comes to students’ disability status and things like that.

David: Yeah, so that’s a real issue. I mean, I think I’m still comfortable with having that policy, but I think you make a good point.

Rebecca: So we certainly encourage folks to read the full report, to dig into the details, but we always wrap up by asking, what’s next?

David: You don’t change the momentum of a university overnight. So I think writing a report is a great step, and now there’s lots and lots of work to be done to both evaluate the merits of the arguments in that report, and to the extent the arguments have merit, to change our culture, and some of those changes come with legislation, which indeed, Harvard has passed. There’s new language in the undergraduate handbook, which is in the report and mentions a lot of the things that I’ve discussed, including other things that I haven’t discussed, like Chatham House rules in class to give people more comfort speaking authentically. Chatham House rules, as I think your listeners know, is a policy of non-attribution so people can say things in class, experiment with new ideas without being afraid that it’s going to show up on a social media post five minutes after class ends with their name attached to it. So legislation, policy making, culture change, rules that align with the messages that I’ve said, like, how much do we allow pass/fai… Harvard is restricting the degree to which students can take pass/fail courses, not eliminating it, but reducing the freedom to do that as frequently as students did so in the past… finding ways to implement and enforce policies that ban electronic devices in the classroom or at least limit their usage, figuring out where the sweet spot is, and all of that, changing the norm so students get comfortable with difficult conversations, not just in the classroom but also in the dining halls, creating a classroom culture where we’re excited to talk with people who disagree with us, rather than horrified to interact with someone who doesn’t share our viewpoints. I’m seeing all these things actually happening in real time. There’s more and more willingness, just in the last year, for people to say what they think, and we have a lot of work to do. It’s not that culture change happens overnight, but we’re, I think, broadly, moving in that direction, even though there’s individual instances where I’m quite unhappy and still concerned about the freedom of speech on our campuses. So it’s sort of two steps forward, one step backward. But we didn’t get here overnight, and we’re not going to get out of the place we are in overnight. Let’s keep working on these issues and thinking about how we make our academic environments healthy and productive and engaged, and how we create classrooms that people are super excited to have the opportunity to enter and sad when the 32 courses are over and they’re off to the rest of their lives. College should be an extraordinary privilege and an academically delightful one, and getting back to that place, if we were ever there, is going to take some work.

John: Well, all the things that you’ve discussed, I think, pretty much all colleges are facing and it’s nice to see the actions that you’re taking. And I think a lot of institutions could benefit from exploring these approaches.

David: Thank you.

John: And we will include a link to the report in the show notes, as well as a link to the New York Times article that provoked our interest in this topic.

Rebecca: Yeah.

David: In the New York Times article, I will say one thing, it puts a fair bit of emphasis on the students. I don’t think that’s the right balance. I think we need to put a lot of emphasis on what the faculty are or are not doing to create a stimulating classroom environment that will, in this dance between the student and the instructor, create a combined commitment to all the things that we’ve discussed, and just saying the students need to be more academically inclined, I think, is the wrong message, and the New York Times article seemed to have a little bit of that tone.

Rebecca: I appreciate your emphasis on actionable items, and as members of our institutions, we can only control the actions that we can take. [LAUGHTER]

David: That’s right, we’re going to make all of this better by creating classrooms that are exciting to be in.

John: And at least move in that direction as much as we can.

David: Yeah, I’m pretty optimistic, actually. I think there are some people that are just brilliant, charismatic teachers, and it’s impossible to copy and paste that. But there’s also a bunch of low-hanging fruit that doesn’t involve being a charismatic instructor, that can create the classroom environment that’s exciting to be in. I actually think that we can create experiences that are very meaningful for our students by sort of working down a long list of check boxes for classrooms that have the right ingredients for engaged learning and active learning.

John: Well, thank you. We very much appreciate you joining us.

David: Thank you. Great to meet the two of you.

Rebecca: Thank you.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

[MUSIC]

422. Negative Examples

To understand what a concept is, we must also understand what it is not. In this episode, Bill Goffe joins us to discuss his study of the effect of the use of negative examples on student learning outcomes.

Bill is a Teaching Professor in the Economics Department at Penn State.  He had previously been one of our colleagues here at SUNY Oswego. Bill is very well known in the profession for his Resources for Economists on the Internet, which was one of the very first internet guides available for economists, and it’s now hosted and sponsored by the American Economic Association. He has served as a member of the American Economic Association’s Committee on Economic Education, the Secretary-Treasurer for the Society of Computational Economics, an Associate Editor for Computational Economics and the online section of the Journal of Economic Education. Bill is currently co-chair of the Liberal Arts Teaching Group, a faculty learning community at Penn State.

Show Notes

Transcript

John: To understand what a concept is, we must also understand what it is not. In this episode, we discuss a study of the effect of the use of negative examples on student learning outcomes.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

John: Our guest today is Bill Goffe. Bill is a Teaching Professor in the Economics Department at Penn State. He had previously been one of our colleagues here at SUNY Oswego. Bill is very well known in the profession for his Resources for Economists on the Internet, which was one of the very first internet guides available for economists, and it’s now hosted and sponsored by the American Economic Association. He has served as a member of the American Economic Association’s Committee on Economic Education, the Secretary-Treasurer for the Society of Computational Economics, an Associate Editor for Computational Economics and the online section of the Journal of Economic Education. Bill is currently co-chair of the Liberal Arts Teaching Group, a faculty learning community at Penn State. You can also find Bill on many listservs and forums devoted to teaching and learning. Welcome back, Bill.

Bill: Thanks, John. Appreciate that.

Rebecca: Today’s teas are:… Bill, are you drinking any tea with us today?

Bill: I am. I’m drinking Twinings green tea, decaf.

Rebecca: Nice, good choice. How about you, John?

John: I am drinking a peppermint tea from the same vendor.

Rebecca: Nice. I have hot cinnamon spice today because my daughter requested a pot of it. So we’re sharing a pot of hot cinnamon spice this afternoon.

John: Very nice as we approach the holiday season here when we’re recording. So we’ve invited you here today to discuss your recent working paper with Austin Boyle entitled “Negative Examples in Lecture Improve Student Learning. First, can you tell us what you mean by negative examples?

Bill: Sure. So you’re describing a concept to someone, let’s say a young child, and describing what a dog is, and you talk about different types of dogs: Dalmatians, Pomeranians, whatever. And unless you describe how a cat is not a dog, they’re probably not going to have a great idea of what a dog actually is. And this might be the case, say, for a second language learner, where you might have different terms for different types of mammals, also related to something called contrasting cases, where experts are very good at delineating different types of phenomena or different types of facts, if you will, say a physician or variation theory used in math. So these are all ways of getting students a rich understanding of something.

Rebecca: Can you talk a little bit about what prompted your interest in negative examples?

Bill: Sure. So I’ve used clickers to teach for a long, long time. You currently use iClicker Cloud. Love it. And I tried to write a paper on just in general, using clickers to teach economics, and the paper didn’t really work very well. There’s reasons it’s never been written, and the reason was I use clickers in many different ways, so a little bit more focused approach to having a paper. And a few years ago, I came across the book How to Teach Economics by Yousuf Hamid. He was a high school teacher in Great Britain, and I had to read that. Well, to be honest, I read it during graduation. You have to go to graduation every year, but, you know, it always kind of work. And he talked a lot about negative examples. And I thought, “Oh, that’s pretty interesting.” And it also followed work by Kirschner, Sweller, and Clark on the idea of direct instruction lowercase “d” and “i”, and I thought, “Well, that’d be an interesting to try, and it’s fairly focused,” and that’s the genesis of the paper, and pulled Austin into it as well.

John: We’ll include citations to those resources in the show notes. So, many people, when they’re teaching, tend to use primarily positive examples. What’s the problem with just using positive examples of a concept? I think you’ve already addressed that a little bit.

Bill: One example, the original work on this, at least formal original work… the idea goes back a long, long time… it was by Engelmann and Carnine, a book on direct instruction. And they give an example of a glem, a G- L-E,-M. Could you define that just with one example? Well, you could not. And they give an example of a pencil. Well, is it wood, or is the pencil horizontal, whatever? But it helps you focus in better on an idea when you understand what it’s not. For example, in economics, we talk about capital quite a bit. Unless you tell students, it’s not stock, they probably won’t pick up on that. Or you give examples of money, how money is very specific to economics. To economists, they’re probably going to pick up on it’s not some general sense of wealth.

John: And I can affirm that the concept of capital is one tht students have always had a lot of trouble about because the way we use it in economics differs from how it’s used in everyday use. And just for our listeners, could you explain the difference between the everyday use of capital and the way we use it in economics?

Rebecca: I’m an everyday listener.

Bill: Great. We’ll see if this works. So, to an economist, capital is something owned by business, manufactured and used to make things, so, a commercial mowing machine, a commercial oven, a jet liner, construction equipment. In everyday usage, it might also include financial capital, stocks, for example, might be considered capital. I would add one thing economists really haven’t done, the Bureau of Economic Analysis actually redefined capital recently, includes intellectual capital, and I just don’t teach that. It would be confusing, but there’s actually about as much intellectual capital these days as physical capital in the United States. But maybe in an intermediate course. I would do that.

Rebecca: Seems like an easy thing to get confused. Can you talk a little bit about how you pick which concepts to use negative examples for? I can imagine that some concepts are more ripe for this strategy than others.

Bill: Yes, we wanted something fairly limited, not a long concept of how to manipulate a graph or something, that could be a further work, but just a definitional sort of thing, but the students have misconceptions about this, as we described there with capital. And so we picked three different ones, money, capital, and technology. Technology in everyday use, our phones, for economists, it’s basically efficiency in the production process. You know, it is rather unfortunate, there’s what, 50,000 words in the English language, and we use words that are used in other ways just to make it more complicated. And I joke to students, I wish I could use different words, but that would hinder their further education, I fear..

John: And this has been a problem for economists for well over a century in those very same issues, and it would be nice if we didn’t have to do that, because it introduces that confusion right when students are just starting to learn the vocabulary of economics, and the way I talk to them about I say, “Hey, part of this is you’re just learning the language of economics, which may look similar to how we use these words in everyday use, but it’s a bit different, and you need to focus on these things because of the ways in which we’re using the term.”

Bill: I would add that I’ve seen some STEM education research papers. They teach concepts first and jargon second, and they find that actually improves learning because you’re trying to remember the terms and they’re using the terms, it overloads your working memory. And there you teach jargon later, they find improvements.

Rebecca: We do that sometimes in design, where we separate the technical skill from the concept, because learning them all at the same time can be complicated. I was thinking of a term that I run into all the time where there’s confusion, where this strategy might work well, which is digital accessibility, where people confuse the idea of meeting these standards to help people with disabilities gain access to content and material with the idea of “just available,” like it’s on the internet.

Bill: I learned that distinction just recently myself. I would add on choosing these terms, we found even after instruction, students got it right between 60 and 71% of the time. So we seem to have picked a nice set of terms and definitions to use.

John: Which is actually much better than they would tend to if you had only used positive examples. And we’ll talk about those results a bit in just a minute.

Rebecca: Can you tell us a little bit about your study? How you had it set up? How many students were involved?

Bill: Yes, we had about 1200 students. I teach large sections, about three sections every semester, about 340 students that did this over two semesters, basically just repeated things, get more power for our tests, and did a so-called crossover study. Crossover study, have your participants that are both controls and the intervention side of things. We did a 3×3 one, which is a little bit hard to describe on a podcast, where you can’t visualize things, but there’s a fairly famous one for ultra processed diets. You might have heard of by Hall and company in 2019. They put people into hospitals, they control directly what they ate, and one group got ultra processed foods for two weeks, the other group got more wholesome foods, I guess you could say, and then they switched who got the wholesome foods, who got the ultra processed foods. So thus you could deal with the fact that people might have responded differently, that different foods have different effects on people. And we did the same thing here in our study, except we had three concepts and three sections and three teaching methods.

John: So what were the three teaching methods that you used in the study?

Bill: So we just use a positive example, where I would list a half dozen examples of capital or money, or not quite of money, because there are quite six examples of that. Then with lecture, we had both positive and negative examples, where these things are money, this is not. This is capital. This is not. And a third type of teaching method was with a cliicker question, where I gave a student’s list of things. Some are positive, some are negative examples, and they just choosed which was which.

Rebecca: So what did you find? [LAUGHTER]

Bill: We found that including negative examples improved learning in the following class by about 21 percentage points. Let’s say the class got 50% correct just with positive examples, the class that got positive and negative examples got a 71% correct. So it is very statistically significant, it was robust with lot different statistical methods we used. So we’re pretty pleased with that, it was a clean result. Sample size helps, and I think probably doing it just with precise definitions, not long drawn out concepts or something like that.

John: Based on these results, would you suggest that perhaps we should all focus a bit more on using negative examples as well as positive examples in our teaching?

Bill: Yes, most certainly it’s an easy intervention. For example, you might know Jim Lang’s book Small Teaching, where he talks about easy interventions you can do, and this one’s easy, as long as you remember to do it. But you’re making up your notes, you think of things that aren’t whatever you’re talking about. You’ve probably seen those on exams, overall. Interesting result we had too that the clicker questions, the active learning, was no different than the lecture. So we’d like to do further work on that at some point, most active learning studies have been semester long, active learning not very well defined, and we didn’t find any difference here, so presumably, at some point, active learning make a difference be interesting to find out what that difference might actually be.

John: That’s an interesting result, and that’s probably worth some follow up.

Bill: I think partly on if you do see similar results with the flipped class and active learning and such, people don’t really have a framework on what they actually ought to be doing before class and what active learning should look like. I’ve got another paper on this, I mentioned, that uses so-called deliberate practice, That’s the 10,000 hour rule. It’s really not 10,000 hours, but there’s some key parts of active learning people don’t do that much. One is to focus on sub skills, for us in economics, things like change in demand versus change in quantity demanded. We generally don’t teach that specifically, is my thinking. And then also, you want to make connections between things. How are supply curves and demand curves separate? How are they similar? Because we remember things in terms of frameworks or schemas. The negative examples help develop schemas. You understand what things are not, kind of triangulating, if you will. And good active learning does the same thing, but if they’re just filling in a worksheet with numbers, they plug into the formula, that’s active learning, I suppose, but not much learning there. So we ought to focus more on exactly what active learning is doing for people.

Rebecca: Following your study, what are some different ways that you’ve incorporated this practice into your own teaching practice?

Bill: So I definitely try to do the negative examples, capturing, oftentimes what I see on these exams and when I’m thinking of a question, I always try to have it connected to something else. Oftentimes one concept’s related to another one, and we make that connection explicitly in clicker questions so that, “Oh, if you remember this, this is a variation of that.” For example, how is consumer price index similar and different than the GDP deflator would be one thing we do, and why are they fairly similar? To understand better how those things work. Oftentimes, I explain things based upon what they’ve done before. Another thing I do is we have very nice technology at Penn State, I have a main screen in the classroom. I have a side screen. Side screens, I put up an outline includes both the outline for the day and our bigger topics. So I’m constantly referring to that outline saying “what we’re doing here is a variation of this, is different than that,” and so forth. And if I do surveys as students, they find that very useful. A better study would be to not have the outline there someday and see how learning goes. I just now thought of that as an idea to do a study on.

John: We know that students come in with a weaker set of connections than faculty, and sometimes we assume that they’re able to make these connections and see the relationships, but they haven’t yet developed that skill, and sometimes it’s generally believed that that type of scaffolding, by putting up an outline of the main concepts you’re trying to get across, can help. But I don’t know if I’ve ever seen any tests of that, certainly not in economics, but I’m not sure if I’ve seen it in other areas either.

Bill: I have not. I can’t say I’ve looked very hard. I would add, to do my literature searches. Now I oftentimes start with ChatGPT, but that’s another whole podcast, [LAUGHTER] I suspect.

John: Yes, and there’s some other good tools out there, too. There’s research rabbit and other things that will actually, even in some cases, will provide more accurate results. But ChatGPT is getting a lot better all the time for these things.

Rebecca: Other than in the test results, have students responded? Or have you asked students about using the negative questions and how that impacts their learning?

Bill: I have not actually, that’s a good point. That’d be another thing to add. It’d be interesting see if they actually can pick up on that. Oftentimes, they’re not that great on understanding their own learning. Something we’ve all seen as instructors, someone thought they did much better on the exam than they actually did, which is something you want to avoid. That’s a tough place to be in and you have to be sympathetic as instructor when that occurs.

John: Now, one thing to keep Rebecca happy, we should also ask a little bit about the methodology that you used. In the study, you used both pooled data as well as panel data, and you also used ordinary least squares and binary dependent variable models. So could you just talk a little bit about why you used multiple approaches and how consistent were the results across those.

Bill: Yeah, the results are very consistent. So sometimes you don’t quite know what econometric approach you take, you can argue this is better, that’s better. And another approach, and it’s pretty common these days, I think it’s by far the most common, is you take multiple approaches and see if the results are robust, to see if you get similar results. Indeed, we did. So it’s a very happy occurrence when that occurs.

John: So were you surprised at the magnitude and strength of these results? I know when I read the paper, I was surprised at just how large that effect was, because it’s very rare to see effect sizes that large in a study of alternative teaching approaches.

Bill: I don’t think I had many preconceptions here. That’s odd. I certainly was pleased, but I can’t say I was quite surprised. It certainly makes me want to take this approach again with where we do the crossover studies, you can do many things with that. For example, we’re told we should teach about things students find interesting. Well, is that really true? I did an example of that this semester where Penn State students, they do a large fundraising project called Dance Marathon, or THON, and last year it raised $17 million, about half the student body is involved. And so I do a question about real prices, and the question was, did THON actually raise more money when you correct for inflation, you know, something they’re invested in a lot, and informally looking at the data, that didn’t seem to have any impact on that segment, on calculating real prices, I need to do more work on that, but I hope more people take this methodological approach, the crossover studies, where you could study a lot of things, the outlines, is it interesting or not, and so forth?

John: Because with the crossover study, you’re ruling out the possibility that there was just some systematic difference between the sections or between the other conditions, right?

Bill: Yes, exactly. We actually found there were differences between the sections. Some did much better on the questions than others, but by effectively pooling these different interventions and controls together, we can adjust for that. We can correct for that. I don’t see it that much in economics. John, have you seen it very much, the crossover studies?

John: I haven’t seen very many, but one that stands out, and I think it was done by economists, was back when the initial studies came out that suggested that when people took notes by hand, they remembered more than when they used a computer. I do remember a study, and I’ll include a citation to this in the show notes. What they did is they had all the students use mobile devices for note taking for a portion of the semester, they broke up the group into two halves, and half of them, for the first half of the semester, took all the notes using mobile devices, and the other half took notes by hand. And in the second half of the semester, they switched, and what they found was there was no difference between how students did when they took notes by hand and how those same students did when they took notes on the computer. However, those students who regularly chose to take notes on a computer, on average, did less well regardless of whether they were taking notes by hand or on a computer, which suggests that the students who choose to take notes by hand may be perhaps a little bit more focused on the material no matter how they’re taking the notes, and those students who may bring in mobile devices to use, perhaps may do it because they prefer having the availability of distractions. So it’s not quite the same thing, but it’s similar in that you’re controlling for the individual effects and separating it out so that you are getting a better comparison, correcting for any self selection in the people who are involved, which is really, I think, what crossover studies are trying to remove, the effect of any systematic selection between the groups.

Bill: I would add, I’ve surveyed my students, and generally my students aren’t performing quite as wel do choose to use laptops, and higher performing students do not, so consistent with your more serious study, for sure.

John: And another thing I’ve been noticing a lot is that for quite a while, I’ve been walking around quite a bit, but I’ve also noted that the students in the front third or so of the class tend to be very actively engaged in answering questions, and they tend to do really well, and they tend to ask more questions. But when I’m in the back of the room, I see a lot of laptops where there perhaps may be a video playing, there may be some online shopping going on, or there may be some sort of video game that they’re working on on the mobile device and their laptop or one or the other. And I think we often do see that sort of sorting in terms of the way in which students choose to work. I’m not sure how I could get rid of that effect, other than moving them all up to the front.

Rebecca: it does seem like at least the multi taskers, in quotes, are at least removing themselves, and because they’re probably distracting to other people, they’re just moving themselves to the back so they’re not a distraction to the people who are trying to focus.

John: Except they’re surrounded by other people, and there may be a bit of a contagion effect going on back there.

Bill: I’ve seen papers on the contagion effect, yes. There was a Carl Wieman paper. They did look at seating in large classrooms and found a very definite effect. They even switched seats halfway through the semester to bring the people in the back up to the front, and they still did worse.

John: Some of our students are just much more interested in the material and much more engaged than others, and there’s just so much we can do with that.

Bill: It’s tough, but I would be a little bit sympathetic to students. It’s been a long time since I’ve had to sit through a typical undergraduate today, and you know, you’re three, maybe four courses a day, a lot of sitting. That would be tough, a challenge. There’s a book called My Freshman Year, and there’s a field anthropologist who went to college for a year to understand her students better, and she found it was harder than she thought.

John: And she found some dramatic differences, too, in the way in which students connected with each other from when she had been a student, that they had much tighter networks with the people they knew before they came to college, because it’s less costly to connect with them over social media and so forth.

Bill: Yeah, friendships have changed with the rise of social media.

John: It used to be costly. I remember having to bring all these quarters to put in pay phones back when I was in school.

Rebecca: You mean, in those 1900s phones that my daughter refers to, John?

John: Yes, I think that would be the case.

Bill: What’s a quarter?

Rebecca: Yeah. [LAUGHTER] Well, I think we still know what quarters are. We just don’t know what pennies are anymore. [LAUGHTER]

Bill: Well, that could be, yes. [LAUGHTER]

John: Are there any other things you’d like to share about this study?

Bill: Well, I think that’s pretty much it. I think I would emphasize this little bit more about, again, developing student schemas. But we talked about that earlier today, this broad network, and it’s through connections, and this helps them make connections more focused on whatever the topic might be at hand. So I think negative examples could be used for teaching most anything.

Rebecca: Including our colleagues when we’re doing professional development and all kinds of other contexts.

Bill: Yes. [LAUGHTER]

John: And it’s easy for us to lose focus, because we already can distinguish these things more easily, and students have not yet developed that capability.

Bill: The curse of knowledge really reigns here, where you know it well. It’s hard to understand how other people don’t. I was doing a think aloud exercise the end of last semester where I interviewed students half the semester in how they’d interpret different things. And I spent a lot of time on aggregate supply, aggregate demand model in my course, standard staple things course, asked about certain phenomena, no one actually used the model. I was only interviewing four students, so, I mean, it’s not much power there, but it was kind of crushing, but it gave me insights on how they think about things.

John: In an earlier podcast with Doug McKee, he talked about having students come in and just work through the problems verbally, so he could see what sort of process they were using, and by seeing that, it was easier to help guide them in a more productive direction. Because if we don’t really understand how students are processing things, it’s hard to help them understand things more deeply.

Rebecca: Yeah, we use that talk-aloud method and design a lot too around troubleshooting, because troubleshooting strategies require, especially around technical skills, you need to think about problems in particular kinds of ways to be able to solve them correctly, or to be able to solve them at all, [LAUGHTER] and doing that kind of talk aloud strategy works really well for figuring out where something went awry in the troubleshooting.

Bill: And experts think a lot differently than students, I would suspect. You would never think someone thought that way.

John: And fMRI studies confirm that different areas of the brain are activated for experts and for novices when trying to solve a problem. So it’s easier when you become an expert, but we have to help students develop the skills to get there. While we’ve got you here, could you also tell us a little bit about how you teach classes so large with so many students, every semester.

Bill: I use Perusall as a way to have them read ahead. They come into class, they have some sense of things and then a lot of clicker questions, typically, as long as it’s not something new. For example, I was doing long-run growth. I do that a fair amount, and the book doesn’t really talk much about the institutions and the role of markets in that. So that was a day or two of me talking. I’m going to be doing the business cycle starting tomorrow, and that’s a fair amount of me talking. But other than that, it’s them pretty much asking questions, or me asking questions. Probably on Thursday, we’ll get to calculate employment rates like read about that in Perusall. I’ll ask them, “Who in this group of people is unemployed?” Then I’ll show them some data, what we compute from that data. So it’s a lot of using ideas. I do a lot of peer instruction where they first do votes on their own, and then I say, “Okay, there’s a lot of uncertainty here. Please convince your neighbors.” And I use an app by iClicker, so I can see their votes on my phone, and then they do a revote, and they usually improve 20, maybe 30 points, then I provide further instruction on the topic at hand. And there’s a couple papers I’ve seen. Let’s see Michelle Smith at Cornell, a computer science paper. They found that if you have students revote, that helps them; if you have a further explanation from the instructor that helps them; if you both, it helps them even more, and it’s probably enriching their schemas, they’re seeing several explanations that reinforce the connections between things. Also I do a series of quizzes, rather than midterms. I do seven quizzes. We’re very fortunate at Penn State, we have a testing center, so I send all my students there for testing. So I run the quizzes for two days, after the quiz they do a second stage of the quiz. In class they start, then they answer at home using any resource, and finally I release the answers, and then they reflect on and describe what they found most difficult on that quiz. So we do a two-week cycle. There’s a lot of feedback, even though the course is quite large, with about 1000 students a semester.

John: So having that testing center gives you a bit more security in the assessments than many of us have when we give quizzes online. Many of the assessments I use are done online, and I used to feel a whole lot better about them before we had ChatGPT and other AI tools appear, especially with some of the browser extensions, which will directly answer things, so you’re able to avoid some of that, which is really nice.

Bill: I really feel very fortunate, yes. I would add, it’s a good thing that the AI tools came after Covid, rather than before.

Rebecca: So one of the things that you mentioned when you’re describing your class was Perusall. Can you describe what that is for people that don’t know?

Bill: Oh, yes. An e-text goes inside perusal, and then students mark it up with comments, questions, and responses to other people’s questions. So it has them thinking bit about the text. It grades them automatically. They need to start early so there’s a little bit of spacing there. They need to make sure they document across the assignment, and they’re also rewarded for opening up multiple times, not just the evening before the exam. And it’s fairly straightforward. Most students get full credit on each assignment. So it’s a bit of encouragement there to read ahead. I used to use so called JITs for just-in-time teaching assignments where I’d ask some questions, including what they found confusing, but I like Perusall better. It’s less grading work, and also, I think it covers a little bit more topics or not just looking at the answers to my JIT questions.

John: Now, do you break them up into groups when they’re using Perusall?

Bill: Yes, Perusall actually does that. When I look at the marked up text from 1000 students, the page does not load basically, or has difficulty loading. And you can also download, I’ve not done this, you could download all their comments, and you can run through a chatbot or whatever to get a sense of what they have trouble with, or what they find confusing. I think textbook authors would find that really, really useful. You could say your textbook was designed with the input from 1000s of students, and so your students can read this more carefully. I’ve not found an author willing to do that, but I think that would be a big win for someone.

John: It could be, and from a marketing standpoint, that could be really helpful. When iClicker announced that they had AI tools being built in, I was really hoping that they would integrate that into the text responses, because right now, it’s just generating questions, which is not bad, and it doesn’t do a really bad job of that, but it would be so nice when you got maybe 1000 or more responses coming in, or a few 1000 responses, if it could very quickly generate a summary of the responses to free-response questions. That seems like something that wouldn’t be all that hard to do, and it would be a nice extension.

Bill: I agree. I think there’s some polling technologies that do that. For some reason iClicker doesn’t. I wonder if there’s a legal issue or some other constraint. I would love to see that as well so I’m not just asking five different possibilities.

Rebecca: So we always wrap up by asking: what’s next?

Bill: So I want to do more work on active learning, the ideas from deliberate practice. Does it make a difference if students have good sub skills. And does it make a difference if you teach by making connections between different topics? There’s many ways active learning are implemented. And it would be cool to do a study where you look at active learning in many different implementations, but some get those interventions, some do not, in a crossover study, of course. So that’s what I would like to work on next a bit.

John: That sounds like a very productive area of research that could really be helpful for all disciplines.

Bill: Yes, well thank you. That’s the idea, I think, a bit about marketing of papers, you know, who would find it interesting, and so forth.

Rebecca: Well, thank you so much for joining us, and we look forward to your future research.

Bill: Thank you so much. It was great fun.

John: It’s great talking to you, Bill, always. I’ve learned a lot from you over the years, and it’s great to still be able to see your work out there in various places.

Bill: Great. Okay. Thanks so much, John. It’s great to connect up again.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing assistance provided by Ryan Metzler.

[MUSIC]

420. Teaching from the Same Side

Antagonistic relationships with students are not uncommon—there is a history of faculty distrust of students. In this episode Michelle Miller joins us to discuss a same-side approach where faculty and students work together in support of student learning.

Michelle is a Professor of Psychological Sciences and President’s Distinguished Teaching Fellow at Northern Arizona University. She is the author of Minds Online: Teaching Effectively with Technology, Remembering and Forgetting in the Age of Technology: Teaching, Learning, and the Science of Memory in a Wired World and A Teacher’s Guide to Learning Students’ Names: Why You Should, Why It’s Hard, How You Can. Michelle is also a frequent contributor of articles on teaching and learning in higher education to a variety of publications including The Chronicle of Higher Ed and is the co-editor with James Lang of the Teaching, Engaging, and Thriving in Higher Ed Series at Oklahoma University Press.

Show Notes

Transcript

John: Antagonistic relationships with students are not uncommon—there is a history of faculty distrust of students. In this episode we discuss a same-side approach where faculty and students work together in support of student learning.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guest today is Michelle Miller. Michelle is a Professor of Psychological Sciences and President’s Distinguished Teaching Fellow at Northern Arizona University. She is the author of Minds Online: Teaching Effectively with Technology, Remembering and Forgetting in the Age of Technology: Teaching, Learning, and the Science of Memory in a Wired World and A Teacher’s Guide to Learning Students’ Names: Why You Should, Why It’s Hard, How You Can. Michelle is also a frequent contributor of articles on teaching and learning in higher education to a variety of publications including The Chronicle of Higher Ed and is the co-editor with James Lang of the Teaching, Engaging, and Thriving in Higher Ed Series at Oklahoma University Press. Welcome back. Michelle.

Michelle: Hi, thanks. It’s great to be here. Thanks for the chance to speak with you today.

John: It’s great to talk to you. It’s been too long. Today’s teas are:… Michelle, are you drinking tea?

Michelle: I am not. I had all great intentions, and one thing led to another, and our time was upon us, and so I will be drinking a nice, tall glass of water

Rebecca: Again, the foundation of tea. I have Lady Grey today, John.

John: And I have ginger peach green tea today.

Rebecca: Nice. So we invited you here today to discuss a workshop that you’ve been offering recently on teaching from the same side. What do you mean by teaching from the same side.

Michelle: So I want to just say right off the top here, it’s not like a formal framework or a theory or something like that, it’s really one resonant phrase that kept coming to the top of my mind to bring together a number of themes and ideas and even philosophies that I’m seeing converging out there in the world of higher education, pedagogy and course design. And so when I look at all the roots, I think of things like Pedagogy of Kindness, Cate Denial’s wonderful book and framework. I think about the idea of relentless welcome that was talked about in Peter Felton and Leo Lambert’s book, even some themes that come up from ungrading and alternative grading, all of which are feeding into this idea of: “Are there strategies? Are there practices that are essentially more adversarial than they need to be in our relationships with students and what might we try instead, and what can be some of the outcomes?” So those are some of the things that pulled together and have started again to come to the forefront of my mind as I’ve talked about this with faculty. So, teaching from the same side, itself, just the phrase, it’s also something that I wrote about in a newsletter issue from a couple of years back, and it keeps coming back to my mind, and I think it continues to resonate with readers of that newsletter. I think I got a lot more traction with readers that I have with some other ideas or themes or discussions that I’ve put out there in the past. That’s what got me thinking a little bit more about some fundamental realignments that can take place in our ideas about why students are taking our classes, our ability to meld those goals and find common ground with the goals that we have for them, and to even interrogate and question some practices that we may take for granted, even things like how we frame policies in our syllabus, what those policies even are, and how we think about drawing students into the concepts and material that we want to share with them as excited, engaged teachers ourselves.

John: It makes so much sense that we should share the same goals as our students, that we want our students to learn, and they want to learn. Yet a lot of faculty come into teaching with this adversarial approach. And Cate Denial talked about that in her book at the beginning, how she was encouraged to not trust students so much. And I remember getting a very similar induction into academia from colleagues. Why is an adversarial teaching approach or teaching relationship so common?

Michelle: Well, I really like how you talked about your formative experiences as a college instructor. And I think even for those of us like myself, where it’s: “ah, that was decades ago. How could that still be affecting me today?” But that’s what formative experiences do. They set us on a track, and they put us in a mindset that may be with us for a lot longer than we realize. So I think it’s really productive to think back, and I encourage faculty to do that as well. When I do get to talk to folks about this and work with them, I say, “Well, what were some of your early experiences, both as a student and as a budding, upcoming instructor?” And yes, like so many of us, I was told explicitly and implicitly at every turn, “Well, you got to keep an eye on those students. Your job is to choose the goals and the content and make the demands on the students.” We are the experts in the room. It makes sense that we would be leaders in that way. But then that turns into the sense of and you can absolutely expect that students don’t want that. I’ve heard different variations on: students just want the easy points, they want to check a box, they want to get a credential, go on to have a job and not intellectually engage. And so, hear that over and over and over, and you’re encouraged, and maybe it’s even valorized in certain cultures, certain departments, certain institutions, that the more you can push that onto students, the better instructor you are. And so I think many of us are questioning that, but we may not have some better ideas or anything concrete to replace it with, or we’re concerned what would happen if I changed this policy or took a different approach? And I’ll say that I also had an experience I’m just realizing now I’ve been thinking back to this experience. It was actually the very first time I ever taught a course as the instructor of record, and it was fraught with all kinds of stresses, as you might imagine [LAUGHTER], and there were definitely times when I felt that what I wanted was mismatched with what students wanted, and that we were on different sides, but it was long enough ago that I’m comfortable even sharing the particular conflict that happened. So I had a teaching assistant in this class, and the way it played out, the teaching assistant was about my same age, which was already kind of fraught enough, so I’m trying to navigate this relationship with him, and he is quite naturally trying to step up and be helpful and tell me what to do and what he thought I was frankly doing wrong in the course. And we had had a lot of student feedback coming in over and over that students, they weren’t happy with the course, but there was one thing in particular that they kind of wanted, and I looked, it was like, “Well, that’s an easy fix.” It just had to do with how many points and how fine grained of feedback they were getting on this one recurring assignment. I was like, “Well, we could do that if we change this midstream and it works better for everybody. Why not?” And my teaching assistant and I had a confrontation in my office. I mean, I remember it quite well. It w as the sort of where you’re both standing up on other sides of the desk and raising your voices at each other. And he said, flat out: “If you do this, regardless of what the change is, if you let those students know that they can ask you for something and that you’ll give in…” his words… “then you’ve lost it. The students are going to be in charge from here on out, and that’s going to be it for this semester. That is what will happen if you do this.” And I don’t see a good reason not to do it, and I’m going to try it, and we tried it. And what do you know? Students were happier and satisfied. We quit getting those same emails every single week, and it was fine. If anything, the level of respect and willingness to put forth productive effort improved. It didn’t go away. So I had the benefit of having that, but it’s something that I still had to tuck away and return to all these many years later and occasionally still do to remind myself that sometimes it’s a matter of just trying it. So even if we’re kind of dissatisfied with an older, adversarial philosophy, there are ways to experiment and find better approaches that meet our needs and our goals and our aspirations as the experts in the room, but also meet students’ needs a lot better.

Rebecca: We see a lot of examples where this us versus them relationship comes up. We see it on campuses, between faculty and administration, between students and faculty in a lot of different contexts. What’s the value of trying something different and not having this us versus them mindset.

Michelle: Well, the other really indelible experience and tipping point that I had around this does it have to be us versus them thought process was during remote emergency instruction. So during the pandemic itself, where that case, I think, like for so many of us, it wasn’t even a matter of like, well, maybe I could take something good and make it even better, that we could accrue benefits. I think we were in survival mode. I know I was. I had a handle on the technology. I think I was probably pretty well positioned, but I still had this experience, as many of us did in spring of 2020, going, “Oh, everything that I planned out and I was going to do and all the boxes I was going to check, because that’s what I always do, we can’t do that anymore.” And I just felt the ground fall out from underneath of me. I’m kind of going, Well, how do I choose? [LAUGHTER] And if we can only do some of these things and not a lot of the others, how can I make the right decisions, and part of it is realizing…[LAUGHTER] giving yourself a little bit of grace, and saying, “Well, you do the best you can, and not all the decisions are going to be right.” But I kept returning to that guiding beacon of, “why did students take this class in the first place?” None of us anticipated how it was going to play out when we all signed on for this. But what were the most important takeaways that they needed and wanted to get? And I just was like, “look, whatever the activity or learning objective was, if it aligns better with that, what brought them to the course in the first place? That’s what we’ll do, and other things are going to take the back burner or go away.” So I think that was one real benefit of when we know we need to revise something and not everything can happen, it can be a prioritization tool. But, what I sense is drawing in some of my fellow faculty to this is the idea of that better and more welcoming atmosphere and the possibility of taking more of our contact and conversations with students out of the realm of like the negotiation over points. “If you do this, I will give you that.” I don’t think any of us are really in teaching and getting inspired by conversations like that. sometimes they have to happen, but to have fewer of those and more of “I am struggling with the material. I’m worried about how this is turning out. I don’t know what to do next,” and me being able to step in and say, “Ah, I can help you,” because we are fundamentally on the same side. And even up to yesterday, when a student was asking me something kind of involved about a test grade, and could we do this? I had to take a breath, step back and say, “Okay, what moves her closer to the common goal of her learning about and getting excited about psychology, which is what we’re doing in intro to psych, and what would move her further away from that?” And yeah, more of a same-side solution, without getting too far into the weeds of what it was, I think we hit on something, and I saw a little bit of a light come up for her as well. Now, I think non-adversarial teaching approaches of different kinds are starting to make it into scholarship and teaching and learning and other sorts of literature. But here too, I want to be upfront and say, Hey, I don’t think that we can point to 20 research studies and five meta-analyses showing, well, if you tweak this one thing about your mindset or philosophy, that students are going to attain X, Y and Z. I hope it happens. But right now, I’m in that, to me, exciting and sometimes intimidating state of like, I think there’s something to this, but we just have to keep working working with it.

Rebecca: Minimally, maybe you’ll just be happier. [LAUGHTER]

Michelle: …minimally happier as an instructor. Yes, that’s not about just saying anything goes or let’s always be buddy buddy, and I never want to have that conversation about your grades. It’s not that so much. I think it’s a matter of balance and realizing how even subtly telegraphing or communicating to students like, yeah, no, I said it was all about the learning, I mean that and the points, all that will shake out [LAUGHTER] in the end, and I’m actually willing to act accordingly and keep on that talking point. That really does start to make them step back and say, like, “Oh, actually, this is different, and I think I like it.”

John: Earlier you mentioned that so many of the discussions among faculty are with perceptions of declining student engagement, less student motivation, and students seeing education primarily as a means to a degree and checking off that box so they can move on to the next stage. And there are some surveys that indicate that students today see less connection between what they’re learning and their future lives and careers. Might the same-side approach help support an environment where it’s easier, perhaps, to motivate students,

Michelle: Right, so student motivation. And here too, it’s sometimes, even to this day, still like a little uncomfortable to me as a cognitive psychologist and one who comes from that tradition and sort of side of our psychology. But, as I frequently share about Minds Online. I realized just in the course of writing that first book, which is very cognitively oriented, we talk about memory, we talk about transfer of thinking skills and all this stuff. But I was like, “Oh, wait, if we don’t also talk about why students are doing all this great evidence-based [LAUGHTER] coursework in the first place, then why are we here?” So yeah, just acknowledging that, and reminding myself pretty much every day as an instructor that yes, I do have to think about this. So there is that perception. And I’m not, first of all going to necessarily just totally turn away from somebody else’s authentic experience and say, “This is what my students are telling me, but I respectfully also submit that when I really started reflecting on it, my experience, even recently with students, has not fit that mold of like, “Well, Professor, can’t you just not take up my time, give me my points and let me go on my merry way to a job.” I have students expressing a very diverse range of goals. Some have not formulated goals. They’re not trying to tick a box to get a job. They don’t know what the job is yet. They’re not even sure what credential they want to get. So I think in the right environment, if you know what to look for, and you’re primed to look for it, that you will see that students they do want to learn. Now do they always act in accordance with high intellectual ideals that we start the semester with? No, and I would submit that none of us do. It’s just not how human beings work. But there is a core and a well of authentic intrinsic engagement. But actions need to follow those ideals. So we talk about motivation. I do keep coming back to and frequently sharing with my fellow faculty two old good solid aspects of motivation theory that just seem to work time and again. And one of them is actually getting a lot of new air time as well, and sort of a revival of interest. So I can share those with you, and I’ll hit the real high spots of these. First of all, self-determination theory, our old friend… and all of the motivation theories, by the way too… they also really bring home an important point, which is not to think about motivation in terms of “you’re a motivated person,” or “you aren’t.” Motivation theory just really looks at the conditions of the environment and the setting, which is great news for us teachers, because students don’t have to walk in, just motivated people. They can respond to the cues and the incentives we give them. So self-determination theory is we’re more motivated when we have autonomy and choice in what we do, we have some sense of mastery, or that we’re getting better at what we’re learning, and there’s relatedness or social components, so somebody else out there cares [LAUGHTER] that I’m getting better at whatever this thing is. And I’ve just seen informally, but it’s a very strong impression that I have over the last few years of like scholarship of teaching and learning and learning and learning sciences research, lots of folks are discovering or rediscovering these principles and say, “Well, what happens if we actually bake these into, say, an online class? What happens to student achievement, especially around the autonomy and choice part?” And that’s one that too, that used to be really tough to do. I tried building all kinds of choices and saying, “Well, maybe students can be graded more on tests or weight their assignments more heavily.” I tried that many years ago, and it was so hard to do and track, and now, with better technology tools, we can say, “Why didn’t you pick this? This is your strength and you want to pursue it. Do that, or here’s a fork in the road, pick A or B.” So those things can really elicit just some basic, more motivated behavior. So there’s that, and then there’s always intrinsic and extrinsic motivation. Lots of us say, like, you don’t have to just rely on intrinsic motivation and hope that it will be there. There’s ways to kind of promote that, and it doesn’t mean that you have to get rid of all grade or other kinds of incentives. So I’ve been playing around with different ways to spark that intrinsic motivation, some sneaky ones that psychologists know about, and also some ones that I think lots of us hit on through our teaching experience, things like introducing surprise or asking students to predict, rather than always just telling them what the outcome is, so that can help build that over time.

Rebecca: Were there some sneaky ones you were going to share?

Michelle: Well, see how I did that. [LAUGHTER]

John: Is there a secret handshake or something needed for these?

Michelle: Oh, it is. It is, it walks a fine line between psychology and perhaps a little bit of manipulation, but people can decide if this is in their comfort zone or not. It has a terrible name, which is the “hypocrisy effect,” but has wonderful outcomes. So essentially, it’s a way to try to induce attitude change by getting people to act in accordance to what we’ve said or done before. So we all kind of naturally like to be consistent. If we say it, we tend to start to believe it over time. So there’s ways of doing this, essentially getting students to endorse or identify for themselves the value of either course features or things they’re learning about. Like I have a colleague here at NAU who, years back, was teaching a first-year seminar and it’s a pop culture topic. I won’t get too deep into what it was, but you might look at that and say, “Oh, sure, it’s inherently interesting.” Then again, students may look at it and say, “Well, maybe I should spend more time actually studying in this required chemistry class” or something. And maybe their families are even saying, like, “Why are you taking that?” So, right from the beginning, what he did is he asked students to write a letter home. I don’t know whether they sent it or not, but this is a classic technique to tap into this. So that was the assignment: write to your family and tell them why you’re taking a class in this thing that, on the face of it, might seem a little frivolous, and talk about the skills you’re going to acquire and how that’s going to help you in everything you’re going to do to make the most of your college education from here on out. So, that could be a way to spark authentic buy-in to: “Yeah, this is why I am actually going to put effort into this class,” or in my case, I hit on this with an assignment that does double duty, trying to get more investment in using class time and staking more of the grade on that without doing very traditional participation grading. So they do a reflection on what they’ve been learning over the last two weeks. And one of the prompts that’s in there does ask them like, “What’s your favorite learning?” and yeah, I’m leaning on them pretty heavily.[LAUGHTER] It’s a little heavy handed to say, “Tell me what you like.” But what does that do? You come up with something. I mean, students do have the option. If they’re like, “this is not doing it for me,” [LAUGHTER] I won’t take that personally, but most do find something. And it’s a good fit for intro to psych, because you do something totally different every week. There’s something for everybody [LAUGHTER] in this course. And so they’re the ones saying, “Well, here’s why learning about Pavlovian conditioning can really help you in your life. Here’s something I didn’t know about our sensory systems that was really insightful.” So instead of me always telling them, they’re telling me, and it should spark more engagement over time.

Rebecca: Good. I want you to get off the hook without the sneaky… [LAUGHTER]

Michelle: Yes, knowing me as you do.

John: In our past discussions and much of your work, you usually focus on the cognitive side of learning, as you’ve mentioned, things like attention, memory, critical thinking. How might teaching from the same side support all of that?

Michelle: Well, I think that that’s just such an exciting perspective on this as well, and that’s something that I’ve really been challenging myself on lately too. Say, are these two separate things? So building in things like more retrieval practice, that active, effortful recall that we know is so much better than just the standard playbook of like, okay, you make a PowerPoint deck and explain it to me, and then I’ll take it home and reread it, which we know does not work really well from the knowledge perspective. Distributed spaced studying is sometimes harder to do as schedules get busy, and transfer and practice, this is hard stuff to actually get in there and keep practicing the skills, including with really messy, challenging problems. All that stuff is evidence-based. You know it works. I know it works. Our students, on the other hand, just they see the effort, or even some other facets of this that dissuade them from it. So the way I’ve been thinking of it lately is like, okay, so we finally have these evidence-based practices going mainstream. Like, really over the last 10 years, it’s this incredible success story with more people, podcast listeners, there’s all this great interest. So that’s wonderful, but maybe the next level is to put more into getting students to internalize those on their own. Because, after all, I mean, these are techniques that even if they’re not built into a class, if I know about them, I can probably bring them in as a student, or even if I’m not in a formal class, I’m in professional learning situation years after I’ve graduated, what better skill to have available to me than knowing how to break something down and say, “Oh yeah, I’m going to use some of these same principles.” And there’s some suggestions from the research literature that when students do have those good experiences and see it in action, that they actually do internalize it, even without anybody standing over them and saying, let’s do this. So I’m challenging myself to say, “Well, can I change my mindset from evidence-based practices, cognitive learning principles…?” These are not things just to do two students, say, “All right, I’ve applied this to you because I know it will help,” but also to say, “Hey, this is why it is the way it is, and isn’t it exciting? Let me bring you in.” And that might seem simple, and some of it does come down to transparency, this concept that many of your listeners may be familiar with, where we just quickly say, “Okay, here’s the assignment, but also here’s why it’s here, why it’s set up the way it is, and how it’s supposed to help you.” There is some research showing that that’s beneficial. Students will sit back and go, “Oh, okay, now I get it, and I’m more invested, and I’m on your side now as well to say I’m going to do this hard project and do it the way that you suggested.” So yeah. There is that. But I think also what I’ve facing up to myself is it’s not always just like, “Well, let me just explain to you a better way to do it, and then why won’t the students do it?“ And perhaps coming away is like, “Oh, they’re lazy. They don’t want to try something new, or they just aren’t listening to me.” Well, think of it in terms of risk. Students, by the time they’ve gotten to our classrooms, they are, of course, not blank slates, especially when it comes to their study techniques. If you talk to students for any length of time, you know that they’ve developed what they believe oftentimes are successful study approaches. And it’s a risk. So it’s really a risk-benefit calculation that our students may be engaging in to say, “Well, not only are you telling me to do this thing that’s hard, to close my book and quiz myself, but that’s going to take away from or replace something that’s worked before. Just because you’re telling me to do this, you want me to put my grades on the line [LAUGHTER] and throw out the thing that got me to here.” So when you see it in that way, you realize that there’s perhaps some different tactics and approaches. And here, some really esteemed cognitive psychologists have picked this up as really a challenge. So Mark McDaniel and his longtime collaborator, Gil Einstein, they’re known for a ton of different applied research findings and discoveries all over. And they stepped back and said, “Oh, wait a minute. This is an example of a dynamic that has been with us for some time in all branches of applied psychology, which is just knowing doesn’t transform behavior. Maybe folks out there are all eating their eight servings of vegetables and 30 minutes of exercise, doing everything we know we’re supposed to do, we don’t do it.” And there’s lots of reasons why, but Einstein and McDaniel said it’s still my sense is a work in progress, but they’ve put together a pretty detailed step-by-step program. They call it KBCP. So knowledge, belief, commitment, planning, I think, is what it stands for. But basically, you can get the sense here that it’s about not just “do your retrieval practice,” but also, “let me tell you about it, sure, inform you. But I’m not going to stop there. Let’s also do some demonstrations. Let’s try it with and without, and we can all see it in action.” So building that really authentic buy-in, and then encouraging students to get out there and try it in higher stake settings. So okay, what do you plan to do for that big exam you have in that other class? And let’s lay out a plan for that, and then come back and reflect and say, Well, did it work the way that I predicted it would [LAUGHTER] as the expert? And now, what do you want to do? So again, it’s something that I think is emerging, and their program is pretty detailed. So I don’t know that everybody’s going to go through that step by step, but the spirit of it is very much with me as a cognitively oriented instructor and researcher to say, “Okay, but how do you really reach people so that they are willing and excited to take it forward” and realizing that students, under the right circumstances, may do that, and that’s a much greater gift to them than just what you’re doing by designing a single class.

Rebecca: One of the key things of that model seems to be the idea, or from my experience, is that ability to achieve something and achieve that success and see it working, so that you then might be motivated to try it again. Thinking about any parent who has a kid that doesn’t like to practice stuff, but if they have some success in seeing that the practice helps them achieve something that they want to achieve, then they’re more likely to do it again.[LAUGHTER]

Michelle: It’s a competence piece. Now you’re putting it that way. Now I’m kind of seeing that connection. It’s the competence piece of SDT, and, yeah, as inspiration to stick with it and do the hard things. And when we’re on the same side, we have our techniques as parents who are also in the motivation business, but I have to feel like we are in a better position to do that when we can help celebrate and say, remember, we are in the same side. And here’s where it was difficult, but here’s where, yeah, you tried it and it worked, and let’s celebrate that win together from the same side.

Rebecca: You’ve mentioned a wide variety of cognitive practices. Can we think about what that looks like in practice?

Michelle: Yeah, and here’s where we can tap a little bit more into my other love within the field of psycholinguistics, but some of this is just basic communication principles as well. So as a piecing together and pulling from lots of different areas, how do we communicate that we are on the same side and establish that atmosphere of, frankly, trust and a non-adversarial approach to these things. Some of the things that have struck me as just, first of all, a lot of this work, a lot of the folks that I would consider to be my models, and the folks I’m looking up to here, they’ve talked about the syllabus as a starting point. So there’s lots and lots and lots of work out there. Sometimes, like, as the work builds up, it’s hard to synthesize, but a few threads that I’ve been pulling out. So anybody who has not read Kevin Gannon’s classic, I think, “How to Create a Syllabus” guide, it came out in the Chronicle of Higher Education a few years back. It was really, I think, one of the first clear, unambiguous, like “we need to do this and not that” in our syllabi, including calling into question, in a good way, a lot of the things that we have taken for granted, and I know I just copy pasted [LAUGHTER] semester after semester, including things like using all caps. It might come from a good intention of like, “this is important, I want you to see it.” But as Gannon so accurately points out, this is how we scream at each other online. And why do that? [LAUGHTER] and it’s just saying is there some other way to highlight what’s important? Or even, I had a faculty member say to me recently, she’s like, “Yeah, for all the complaining about, oh, students don’t read it, students don’t get it. Well, think about the policies we deal with. When do we look at them? …was when we need them.” So yes, absolutely, students should read the thing from top to bottom. This faculty member, she even sends out, pieces out, like relevant parts of it as reminders or announcements at the right times of the semester. So there’s other ways to emphasize besides just the all caps and Gannon also talks about is like, is there some way that your syllabus is kind of getting across that, not only here’s the things I don’t want you to do, but there’s no reasons for it other than I say so and it gets across this idea that “and I expect you to do all these bad things.” So playing around with ways of dialing that down. So syllabus warmth is another major concept, some interesting findings about how sensitive students are to that. And then lastly, I ran across, just in the last two years, a great article came out, maybe we put in the show notes. It was focused on STEM syllabi. And here they just took a very open approach, and they surveyed a variety of syllabi to say which of these seems more supportive and inviting and warm to students? And they have a great list that they laid out, including things like just phrasing things as we: we will, we are going to engage in these difficult topics, and here’s how we are going to do it, instead of students must… [LAUGHTER] which is what used to be in all of my syllabi, talking about challenge in positive ways. And here’s the resources that we want. And again, sometimes, yeah, it’s a good thing to have, like, “well, here’s the tutoring center.” But to say, “hey, successful students go to the tutoring center, and I promise you, [LAUGHTER] they will be supportive,” …some other way to do that and encouraging, very explicitly, that we do want students to come talk to us. So yeah, it’s like, well, I said what my office hours were, and now I’m sitting here, and why won’t students come talk to me? [LAUGHTER] And now I have several other alternative ways to schedule appointments, and I do give a little rah rah, but I also back up that verbiage with “and here’s even more access to me and all the ways to do it and in the circumstances under which you should do it.” So that’s one big starting point, is the syllabus. We can definitely get into a few others. But if you start there, I think a lot of other things will fall into line.

John: And we should mention we have a few past podcasts that address some of that. We’ll include some of the links in the show notes related to effective practices in creating that same side environment in the class using the syllabus.

Rebecca: One of the things that I think I’m hearing you say is that to be on the same side doesn’t mean we don’t hold boundaries, it’s just how we communicate those boundaries.

Michelle: Yeah, absolutely. And to pick up on that, run with that a little bit, that is now a really important part of my practice as well. While I’m going through the syllabus and cleaning it up and fixing it up, as far as tone, I’m also questioning the policies, so things like I’ve written about being pretty critical, and I don’t do anymore, the 10% per day deduction for deadlines. I handle deadlines very differently now, which is not to say I don’t have any or it’s anything goes, but I think it’s more the process of questioning it, of saying, “Well, which deadlines in this course are actually fixed, and have I explained that to students or mentioned it in any way to differentiate those where there is a firm deadline, like you really do have to get your discussion post in by Sunday night,” because, Haven’t we all been in an online discussion and somebody answers you three weeks later and we don’t actually have a conversation? Didn’t take forever, but that’s just a little bit of saying, “Yeah, it’s not just because I say so.” So yes, as we get into the substance of it, that is where we do have to think pretty deeply about what appropriate boundaries are. And just for the record, too, it doesn’t mean being buddy buddy, and we’re all friends with students at all times. I’m pretty formal in my interactions with students. That’s appropriate for my style and my persona. But I would challenge listeners, go back over a syllabus, as you’re planning for next semester, identify at least one thing to say, “Is there some way that this could be less harsh?” Perhaps even, as I mentioned, I have my little heuristics for saying, “Well, do I say yes or no?” Does it take the student closer to the learning goal or away from the learning goal? If it’s an assignment that’s intended to develop their skills, so it’s a formative assignment somewhere in the middle of the semester and it’s late, I mean, if the goal of that is to engage them in the learning and that’s why I worked so hard building in the first place. Well, if they’re five minutes past the deadline, don’t I still want them to do it, maybe I still want them to do it a week after the deadline. So with that learning and the shared goals and those shared ideals as the reorientation point, that’s what we can go through and do. So yes, it doesn’t mean throwing out grades, necessarily, unless you want to bring in ungrading for other reasons. It doesn’t mean that now we’re all completely informal, and it doesn’t mean that there aren’t any hard and fast boundaries in the course. But I’ve thought those through and not simply just transferred them over, because that’s what we always do, and if we don’t, students will take advantage of us.

John: One other thing that we’ve talked to you about in the past is the importance of learning students’ names. Could you just talk about that a little bit, and we’ll include a link to your book as well as the earlier podcast on the topic.

Michelle: Yeah. So I mean, I guess if we trace the whole evolution of how to get on the same side or begin that in our instruction. It starts with me thinking about my past and what do I believe is true about students, and then looking in at the overall policies and the objectives of the course, then getting into the communications, and then I’m in the classroom with them, and if it is a face-to-face class, one of the very best, often surprising to students, and effective ways to make a personalized classroom atmosphere is to know and use students’ names. And so that’s also something folks who’ve followed my writing or things I speak about know, that’s something that I’ve really been on a mission to do in my own practice, and to help others know how to do it as well. Now, that said, it’s very, very difficult. That’s one of the themes of the book. If folks see it right off the top, it’s like, and if you’re one of the many people who sidle up to me in various settings and go, “Oh my gosh, I have to tell you something. I’m not good at learning names,” like, okay, most of us are pretty bad at it. It takes effort, and that can be an effort that we model. I mean, even as I circulate around in my classroom, if it’s a big class, we’ve got little table tents with names, which is a great starting point, but I want to eventually know the names, [LAUGHTER] so I go around to the little groups during small group discussions, “Okay, turn your table tent over. I’m going to practice.” And they see me making mistakes. They see me putting effort into it, and see me struggling a little bit, which maybe also establishes a little bit of common ground there as well. But if we put in that effort, I guarantee you that not every student, not every time, but over time, you will see a definite trend towards students saying, “You know what? I think I will listen to what she’s saying about the value of this course. I will follow her advice when she says, ‘study it this way, not that way,’ even though my way is harder, I’m going to stick with it when times are tough. And yeah, I’ll do what she says and try to focus on the ultimate goal of learning and attaining what this class is all about, and not just about taking off points and making sure that I can just put in less effort.” So I think that that is definitely of a piece with the rest. And I hope that folks, whether it’s through the advice in my book, or a lot of other good advice out there, or their own techniques, will keep working to get a little bit better every single semester.

Rebecca: I think we have lots of opportunities now to think about how to work more from the same side, but we always end by asking: “What’s next?”

Michelle: What’s next? Well, I’m going to keep working with this idea and seeing how I can bring it into the classroom and keep an eye out for the great research that’s out there, and continue to share that in the newsletter that I put out to faculty and instructional designers and others who care about learning. I will be speaking about this or offering me an opportunity to be part of a workshop… upcoming POD conference. So for those for whom POD is part of their professional life, they might want to check that out. And I will be going into a new role that I’m excited to take on at my institution. It’s a co-directorship position where we’re going to be looking at AI in the curriculum and bringing some of the developments and robust debate in that side of instruction right now, it’s part of, I think, all of our lives, bringing that into my professional life at NAU as well.

John: Well, that’s an area where there’s so many interesting challenges and such a rapid pace of change. That should be an exciting thing to be working on.

Michelle: Yep, all the more reason to get on the same side, don’t you think?

Rebecca: Definitely, and you won’t be bored, that’s for sure. [LAUGHTER] Lots of motivation to get involved in that work.

John: And I wish I could make it to the POD thing, but I’m going to be at the OLC Conference, which, unfortunately, is at the same time this year.

Michelle: That’s how it shakes out some years. We make our choices the best we can.

Rebecca: Well, thanks again for joining us, and hopefully we’ll get to talk to you again soon. Thank you.

John: Thank you, Michelle. Again, it’s always great talking to you, and I also hope we get to talk very soon.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing assistance provided by Madison Lee.

[MUSIC]

419. Empowered

Women faculty of color in higher ed generally receive little training to prepare them for the challenges, unrelated to their teaching quality, that they experience in their careers. In this episode, Chavella Pittman joins us to discuss her new book, Empowered: A Woman Faculty of Color’s Guide to Teaching and Thriving, a resource she created to address these challenges and to help female faculty of color thrive.

Show Notes

Transcript

John: Women faculty of color in higher ed generally receive little training to prepare them for the challenges, unrelated to their teaching quality, that they experience in their careers. In this episode, we discuss a resource designed to address these challenges and to help female faculty of color thrive.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

John: Our guest today is Chavella Pittman. Chavella is a Professor of Sociology at Dominican University. She is also the founder of Effective & Efficient Faculty, a faculty development company that works with faculty and campuses across the country to help them develop strategies for creating an environment in which all students and faculty can thrive. Her research interests and expertise includes higher education, interpersonal interactions, research methods, and statistics. She is the author of Empowered: A Woman Faculty of Color’s Guide to Teaching and Thriving, which has recently been released as part of the Oklahoma University Press series on Teaching, Engaging, and Thriving in Higher Education, edited by James Lang and Michelle Miller. Jessmyn Neuhaus will be filling in for Rebecca, the usual co-host of this podcast, because Rebecca has been called away by our campus administrators to do administrative things all day today down in Syracuse. Thank you, Jessamyn, for joining us, and welcome back, Chavella.

Jessamyn: Thanks for having me.

Chavella: Yes, thanks so much for having me here. I’m excited.

Jessamyn: Today’s teas are:… Dr. Pittman, what are you drinking today?

Chavella: I’m trying to get a little fancy because I’m usually drinking water, but I have fireside vanilla spice tea.

Jessamyn: That sounds like a nice fall tea.

Chavella: Exactly, exactly.

Jessamyn: I am drinking tropical hibiscus tea from my local grocery store chain, Wegmans, and I’m drinking it in my Tea for Teaching mug, one of my prized possessions.

Chavella: I love it. [LAUGHTER]

John: And I am drinking a spring cherry green tea from the Republic of Tea, which I picked up at Wegmans down near Syracuse. So we have that in common there.

Jessamyn: Wegmans, do you want to fund tea for teaching? [LAUGHTER]

John: They have a very nice collection of teas and pretty much everything else. And I’ve been very pleased. I spend the summers in North Carolina, and there are three Wegman stores within about a 20-minute drive of Duke University now, which is a very nice improvement.

Jessamyn: We’ve invited you here today to discuss Empowered. Can you share the origin story of this book?

Chavella: Yes, so I would say the short version of it is that there are just a lot of gaps in the support provided for women faculty of color’s teaching, so whether it’s colleagues, the literature itself, the scholarship for teaching and learning, faculty development centers, there are just huge gaps. I mean, that’s how I started doing this work as a grad student. I got recognized by other grad students and faculty too for being adept at helping people navigate some of the challenges they were having in the classroom. And so I was giving workshops at departments, I was being a part of the training for TAs, meeting with faculty to help them navigate a range of challenges. Thirty years later, and most people haven’t gotten better at it. So, [LAUGHTER] I ended up writing the book because I was just like, “Wow, like, how do these gaps still exist?” I wanted to get all that information in one place so that people could have a mentor available, 24/7, so that they could just pick the book up if they were facing something in particular. So essentially, that’s the origin story of the book. And I do want to be clear that when I talk about the challenges that people are facing, they’re not anything that have to do with their teaching quality, like I pretty much became adept at helping people deal with teaching challenges that have nothing to do with their teaching quality and everything to do with other people’s expectations of them and social norms. So that’s essentially the origin story of the book, it’s me trying to fill a gap that has remained for 30 years.

John: Now, you’ve already partially addressed this, but who is the target audience for this book?

Chavella: Well, the cheeky answer is everybody. I really do want everybody to read it, because there are lessons in it for everybody, of course, but the audience that it’s centered on is definitely women faculty of color, their experiences often get invalidated or they’re silenced or they’re marginalized. And so I wanted a book that was squarely focused on their experiences, their teaching, their careers, which isn’t to mean that other people can’t get something valuable from it, like there are lessons that everybody can get from it about their teaching. There are lessons that everybody can get from it about how to navigate some of the challenges that they could be facing that might be similar for women faculty of color, but the book is very much so wanted to center women faculty of color, so they’re the primary audience, but the secondary audience are allies that want to support those people and the other secondary audience are just other folks that want to have great teaching and who want to thrive instead of surviving, because the book is very much so about thriving and having like a sustainable academic life and having joy as well.

Jessamyn: What would you like readers to take away from the book?

Chavella: Well, I would say a couple of things. I want people to feel empowered. So I want them to take away that they can feel empowered, that the things that they’re facing have solutions, essentially. That’s part of the empowerment is that you don’t have to just put up with it, or suffer through or muddle through, that you can be empowered, that the things that they’re facing, they can absolutely address. I want them to take away that they can champion their teaching in reviews. So it’s not about just sort of like slinking away and sort of managing some of the stuff that’s going on. It’s very much about proudly proclaiming in reviews like these are the things that I’m bringing to the university, because that’s part of the reduction of stress. It’s like not just managing the stuff in your classroom, but knowing that these things aren’t going to bite them in the butt when it comes time for tenure, promotion, or any sort of retention or renewal review. So I want them to know that they can champion their teaching in reviews, and I want them to feel equipped to teach authentically in a way that amplifies their gifts, in a way that allows space for joy. So instead of having to modify their teaching, or dumb it down, or do the things that people expect, even when those things are not considered to be good teaching, like people are encouraged to do things that are not evidence-based, and women faculty of color are doing things that are evidence-based; they just might be less common. And so I just want them to teach more authentically and joyfully and have space for joy, essentially. So that’s what I want them to take away.

John: You talked a little bit about some of the topics that you’re addressing. Could you give us an overview of some of the other broad topics that are being addressed in the book?

Chavella: As faculty developers, I think that we have the vocabulary for talking about teaching challenges that faculty often don’t have. We think about women faculty of color, and all faculty actually situated in their disciplines, whether it be chemistry or math or English, history, psychology, political science, they don’t have the vocabulary that we have. So I very intentionally don’t introduce the chapters by the threat. So what I do is I sort of describe it. So each chapter opens with a scenario, and the scenarios are meant to be relatable. So every single scenario is something that I’ve either seen in a faculty client or it comes straight out of the research, and it’s something that I want people to read that chapter and be like, “Oh my goodness, that’s exactly what’s happening to me.” And I know, just from consulting for a while, they’re going to relate to that more than me giving the jargon. If I give the jargon, people are like, “Oh no, I’m not experiencing that.” And I’m like, “Okay, I know you’re experiencing that, [LAUGHTER] it’s just that the label that you don’t want to sort of be associated with, or you just don’t recognize it.” So it’s stuff like that. So, for example, chapter two in particular, where I’m telling people to anchor the strong points, I do that in two ways. So there’s a scenario. So I describe something that someone might be facing. So I’m not going to read the whole thing, but I’ll just give you a little bit of it. It starts by saying: “We all know her. In fact, you might know her as you, whether that’s you as you are today, or maybe a younger, more naive version of you. Who am I talking about? The person who sits on every committee as the diversity person, the person who’s asked or coerced to spearhead new initiatives, the person who goes beyond the call of duty to serve students, including students of color, outside the classroom, without being asked and without recognition. Inside the classroom, you’re going all out for these students. You’ve been guilted and pressured to participate as the only woman,” etc, etc, and then says here: “But it gets worse. Despite unflagging extra effort on a part of this woman of color, you guessed it, the University delivers a crushing blow. It’s a ‘no’ to her tenure application, and it’s about her teaching.” So there are more scenarios like that. You get the feel from that one. And this chapter is about extra labor. It’s about additional teaching labor. So someone may not know that as invisible labor. They might say, “Oh, I like doing that. I don’t mind doing that. I’m committed to doing that.” So the chapters open up like that. There’s also another little descriptive piece that opens the chapter: “Gain crystal clarity about your teaching labor, and don’t let your generosity continue to go unnoticed. Reap well deserved benefits from this labor that steals time from research, writing, and grants and, at the same time, banish accusations of bias or an agenda.” So the chapters are all about challenges like that, if that makes sense. I guess the point that I’m trying to make is I don’t want to use the jargon. So the chapters are about challenges such as teaching labor, interactions with students in the teaching, interactions with colleagues about the content that you’re teaching, about expectations for student care. So broadly, that’s what I’m talking about. But I know for a fact that a lot of women faculty of color, shoot, and even the administrators reviewing them, that’s not going to click with them, because they’re not developers. Seeing the descriptions, the outcomes of these things, that’s what’s going to click for them.

Jessamyn: So, thank you for describing the organization of the book and some of the things that are going into the chapters. Can you tell us more about the structure of each chapter and how you hope readers will read and benefit from how those chapters are structured.

Chavella: For sure, the organization of the book is incredibly intentional. I want the book to be used; I don’t want it to just sit on a shelf and gather dust. I want people to be able to use it. So I hope that people read it from beginning to end, but it doesn’t have to be read that way. So, with that in mind, each chapter has the same sections. So, for example, each chapter has a section that’s about the threat. So that way, I’ll describe the common teaching threat that women faculty of color and other faculty might be facing. And the goal is for them to be able to name it, for them to be able to see it, for it to be visible, so that they understand that it’s a structural issue, not a personal failing. Each chapter also has a strategic action. I don’t want to just put threats out there and people not know what to do about them, [LAUGHTER] so I walk them through an evidence-based strategy for dealing with that particular threat. Not only so it doesn’t just tank their teaching, but so it doesn’t tank their reviews for renewal, retention, tenure, promotion, etc. And the strategic actions that I describe, they don’t take a lot of time and energy, because I know women faculty of color don’t have time for that, so they’re streamlined, they get to the point. And each chapter also describes a mine field from the scholarship of teaching and learning. So the scholarship of teaching and learning is incredibly instructive for the evidence-based strategies. It’s just not often a fit for women faculty of color, and so that section waves a flag so it tells them like, “Okay, yes, here are the things you’re doing that are great according to the scholarship of teaching and learning, but here’s how they set up a trap, or how they have a gap that could harm you in your career and your teaching, essentially. And then I have a section in each chapter empowering actions that they can take that week. So if they’re not ready to do the strategic action at that point, there are smaller, micro actions that they can take just to feel empowered in the moment. Each chapter also has a summary that’s sort of like high-level takeaways of all of those things that I just described. And the thing that’s interesting is that even though women faculty of color, like the research demonstrates, are doing all this incredibly wonderful teaching with all of these great outcomes for students, for society, for the universities, there are these nagging, persisting narratives about their teaching as deficient. And so where that comes to play in a chapter, there’ll also be an extra section called “the excellence of women faculty of color’s teaching.” Because, you know, the three of us, we know that literature, like we read this stuff all day. But again, if we think about faculty and the administrators that are reviewing them, they don’t know that literature. So in chapters where there just happens to be a narrative of women faculty of color’s teaching as deficient, there’ll be an “excellence of women faculty of color’s teaching” section that outlines the research of how their teaching is actually stellar. So each chapter has all of those sections, and so that’s how I want people to read the book. I would love it if they read it from beginning to end, that would be awesome. And even after they read it from beginning to end, maybe they faced the challenges that are in chapters one, four and five, and then at another time, they have to flip back to chapter three. I want them to be able to immediately get in and find what they need, particularly if we think that some of these faculty might be under duress, they might be under threat, they might have a review coming up. So I want them to be able to navigate and find what they need quickly at a whim’s notice, essentially.

John: At the start of this conversation, you mentioned that there were a lot of gaps in the materials that faculty were provided. Could you talk a little bit more about the unique contributions of your book to the literature to fill in some of those gaps?

Chavella: I don’t want to give it all away. I want people to read the book, but I would say one of the unique contributions of the book is that I am suggesting that people thrive, that they’re not just surviving, that they care about their well being, that they’re not martyring themselves for the job, for the students. And that does not mean that you don’t still do a great job. It doesn’t mean that you don’t care about student learning. It doesn’t mean you don’t care about your work. Quite the opposite. There’s an entire chapter in the book that’s about joy. So I think that’s one of the large unique contributions of the book. And just given this particular moment, first of all, it’s a moment that other people have been in for a long time… that have been in before, you know, if you think sort of cyclically, and the messaging, I think that most people give is people to just sort of hunker down and slug it out and suffer, and I’m not saying that. So that’s definitely a gap in the scholarship of teaching and learning is that we don’t talk enough about faculty well being. And I very much so want women faculty of color to be well, to be authentic, to be joyful, and all faculty to be that way. So that’s definitely a unique contribution of the book. Another thing I think, I talk about a couple different sort of terms in the book as well, like I’m introducing a couple ideas. We referenced one of them before, and so I talk about what I call super invisible labor. We talk about invisible labor in higher ed all the time, a lot of time it’s about committee work. We do not talk about it as it relates to teaching. So super invisible teaching labor, like I talk about it at length in the book, I talk about what it looks like, the consequences of it for faculty careers, for student learning, for the university, etc. So these are the sort of gaps in the scholarship of teaching and learning that people will find in the book, some unique contributions that it has.

Jessamyn: I love both of those ideas, and just on an emotional, visceral level, talking about joy and thriving in academia, it feels radical, but it shouldn’t be. It shouldn’t sound like such a major issue that’s never been talked about, but it should be… recognizing what you do well and how to achieve that important work-life balance and thrive.

Chavella: Yeah, for sure, all of my work from the beginning, has always been about having a boundary, trying to be as efficient and excellent as you can be, getting back to time with your life, doing things… if you want to use that time for research, if that’s what gives you joy, like, do it. Like, that’s been the messaging throughout. And I definitely feel like that’s the part of my messaging that gets shushed, right? Like people are like, “Shhh, don’t say that.” Like, it’s shameful to want to care about your well being, that it’s selfish to care about your well being. And I’m sick of it. Actually, [LAUGHTER] I’m just sick of it. So it’s definitely the thing that I’m yelling from the rooftop at this moment. I’m always yelling something from a rooftop, and this seems to be the thing that I’m yelling from the rooftop, is that faculty well being is important. It matters. It should just matter, because we’re human, [LAUGHTER] and people should care about us. But for those who don’t care about that, it’s important because it does have a lot to do with creativity and output and productivity and all the things that we can do. So we’re much better at our jobs when faculty well being is good. And again, that’s just the argument for people who don’t actually care about us as humans.

Jessamyn: And it’s rooted in recognizing and helping other people recognize what they’re already doing well… such an important part of educational development. But often people come to the centers for teaching and learning with this deficit framework, like, I must be doing something wrong, or there’s something I have to add, and part of our job, it’s so important, is saying, “Let’s look at what you’re already doing well,” and this is why.

Chavella: Yeah, absolutely, it’s interesting. You triggered another thought in me is that it’s one of the reasons that the book is necessary. The origin story of the book is, I think that teaching centers and developers do a good job of that sort of diagnosing, the: “Here’s what you’re doing wrong, here’s something you could do better,” but they’re not as good at “You’re already doing it excellent, and people are resistant to it. Now what?” And I’m not blaming the scholarship of teaching and learning or teaching centers, because they have limited resources, time and energy, and a lot of times they’re put in the position to be a bit remedial or to teach people new things, they just don’t have the space to help people who are already doing it great. They just don’t have the space for that. So I’m glad to fill that gap and to be a partner with faculty developers and teaching centers and say, “Okay, I got those people,” and a lot of times those people are women faculty of color.

John: One of the things you note is that a lot of the characteristics of the teaching of women faculty of color are undervalued or are not recognized. Could you talk a little bit about what are some of the strengths that appear in the way in which women faculty of color teach, but are not always recognized by the institution or by students.

Chavella: Of course, that’s easy. [LAUGHTER] Women faculty of color, our teaching tends to have more active learning. We know from the scholarship of teaching learning the benefits of that. They tend to have learning outcomes, again, active learning, evidence based, you know, the scholarship of teaching and learning. They tend to have learning outcomes such as maybe like development of social and personal responsibility, and these things are very much so aligned with General Education says we should be producing in our students and with what our institutions want produced in our students. They tend to focus on more skills like creative problem solving. They tend to have assignments that are more applied. So instead of just memorizing, they’re actually learning to apply skills. And again, these are all things that are aligned with university missions, goals, and part of sort of the purpose of education. So those are just a couple of examples. There’s much more detail and citations and statistics and stuff like that in the book, but just sort of broadly, whether it’s their pedagogy, their learning outcomes, or their assignments, they’re doing, all the things that the scholarship of teaching and learning and that the university say, and that the employers also say that they want from students and from society. So they’re doing all the things that produce all the outcomes that both society and universities want, essentially.

John: And hearing you say that… last night, we had a meeting of our student committee on learning and teaching here on campus, and we asked them for some of the things they observed in classes that were not very effective, and the most common response was that a lot of faculty are still spending most of their time lecturing, and they said it was really hard to maintain a focus for 55 minutes, or an hour and 20 minutes of someone just talking at them. So anything that encourages more active learning, I think, would be very much appreciated by students, as well as being a much more effective way to encourage learning.

Chavella: You know, what’s so interesting about that is, yes, obviously we all know that we should have more active learning. Lecturing can be done, like interactive lecturing. Like, I’m not a no to lecture person. I encourage people to do lecturettes, is what I call them, on particular types of concepts, like, it has a place, but you can do an interactive lecture for 55 minutes, and very much so keep students’ attention. This is an example of what I mean when I say that there’s a gap in supporting people who are doing it right. Students might not like lecturing. They’re used to lecturing. When you lecture, they complain. When you don’t lecture, they complain. [LAUGHTER] So that’s a really, really good example of some of the resistance to innovation that women faculty of color face in their teaching is because they’re not just lecturing for 55 minutes, they’re doing other things, and because it violates students’ expectations and colleagues’ expectations of what’s supposed to be happening in the classroom. It’s violating sort of the social norms. They already don’t trust those faculty for a variety of reasons, or may not trust those faculty for a variety of reasons. They complain. They’re suspicious when they don’t find lecture, even though we know it doesn’t produce a lot of the outcomes that we’re interested in, and then students complain about it. So that’s a really good example.

Jessamyn: And that’s such a good example of why your book is empowering, because knowing ahead of time that there’s these things at play that student resistance will flare up, so enables the strategic approach and the mindset and everything we need then to go into the classroom and be effective.

Chavella: Yes, very much so, into the classroom and into reviews. [LAUGHTER]

Jessamyn: That’s right. Absolutely.

Chavella: Like you said, it’s like a toolkit. You’re like, “Okay, I know these things are coming, but I’m prepared, and so I can skip off into the sunset…” [LAUGHTER] not that extreme, but that’s exactly what I have in mind for people. Stuff is going on around you. Of course, we acknowledge those things, we don’t act like they don’t exist. We name it, we prepare for it, and we carry on with the things that bring us joy.

John: Joy is good.

Chavella: Yes, it is… [LAUGHTER] essential.

Jessamyn: Well, John and Rebecca always end the episode by asking their guest, what is next?

Chavella: A book tour. That’s what’s next. So I’m very much available to campuses for virtual and in person, like book club discussions or book talks or seminars or workshops on one or more of the chapters. So that’s what I’m really focused on now, is engaging in a book tour. So if people want to bring me in, either virtually or in person for any of those things, book club sort of discussion, book talk, or some sort of workshop or seminar, I’m available.

John: Well, thank you. I very much enjoyed reading your book, and I hope many more people will.

Chavella: Yay, I hope so too. Thank you for having me. This is always so much fun.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

[MUSIC]

415. One Classroom at a Time

Students have varied levels of preparation for traditional types of classes and assessments used in colleges. In this episode, David Gooblar joins us to discuss a variety of instructional strategies that we can adopt to help all students succeed.

David is an Associate Professor of English at the University of Iowa, a regular contributor to the Chronicle of Higher Education, the creator of Pedagogy Unbound, and the author of The Missing Course: Everything They Never Taught You About College Teaching. His most recent book, One Classroom at a Time: How Better Teaching Can Make College More Equitable, was released in August 2025 by Harvard University Press.

Show Notes

  • Gooblar, D. (2025). One Classroom at a Time: How Better Teaching Can Make College More Equitable. Harvard University Press.
  • Gooblar, D. (2019). The missing course: Everything they never taught you about college teaching. Harvard University Press.
  • Cohen, G. L., Steele, C. M., & Ross, L. D. (1999). The mentor’s dilemma: Providing critical feedback across the racial divide. Personality and social psychology bulletin, 25(10), 1302-1318.
  • Yeager, D. S., Purdie-Vaughns, V., Garcia, J., Apfel, N., Brzustoski, P., Master, A., … & Cohen, G. L. (2014). Breaking the cycle of mistrust: Wise interventions to provide critical feedback across the racial divide. Journal of Experimental Psychology: General, 143(2), 804.
  • Canning, E. A., White, M., & Davis, W. B. (2024). Growth mindset messages from instructors improve academic performance among first-generation college students. CBE—Life Sciences Education, 23(2), ar14.
  • Elizabeth Canning (2021). Faculty Mindset. Tea for Teaching podcast. Episode 212. November 3.

Transcript

John: Students have varied levels of preparation for traditional types of classes and assessments used in colleges. In this episode, we explore a variety of instructional strategies that we can adopt to help all students succeed.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guest today is David Gooblar. He is an Associate Professor of English at the University of Iowa, a regular contributor to the Chronicle of Higher Education, the creator of Pedagogy Unbound, and the author of The Missing Course: Everything They Never Taught You About College Teaching. His most recent book, One Classroom at a Time: How Better Teaching Can Make College More Equitable, was released in August 2025 by Harvard University Press. Welcome back, David.

David: Thank you so much. I’m very excited to talk with you today.

John: We’re very happy to be able to talk with you again. Our teas today are:… David, are you drinking any tea?

David: I am. I’m drinking an I love lemon.

Rebecca: I have a Jasmine green today,

John: And I have a tea in my pocket, but because my meeting ran late, I am drinking diet Pepsi.

Rebecca: Oh, John. [LAUGHTER]

John: I think it’s only the second time in the podcast I haven’t been drinking tea, so

David: I won’t take it personally. [LAUGHTER]

John: I have it here. In fact, it was a tea that was given to us by one of our listeners in France, but I’ll save that for next time. [LAUGHTER]

Rebecca: So we invited you here today to discuss one classroom at a time. Can you tell us the origin story of the book?

David: Sure, back in probably 2019 I was working for the Center for Advancement of Teaching at Temple University. And as part of that job, I was doing a lot of reading of pedagogy research, and I was, of course, coming across a lot of research on ways to close achievement gaps within the college classroom. There was a lot of work being done on how to help marginalized students succeed. And it struck me as I was reading all this that I wasn’t seeing this sort of knowledge base being applied in the work of DEI offices across the country. It was a kind of high time for DEI offices. There was a lot of action, particularly starting in 2020. Universities, responding to Black Lives Matter and responding to a lot of activism, looking to reform universities, and so DEI offices were doing a lot to try to change the cultures within their institutions. What I wasn’t seeing was DEI offices doing really anything to change how professors teach. And that sort of contrast, looking at this sort of growing base of knowledge that the way that we teach can actually change student outcomes versus the part of our universities sort of devoted to remedying the inequities in student outcomes, sort of ignoring that. That was the kind of conundrum that started my research and really got me thinking about how can I close this gap, how can I communicate with the people who need to hear what they can do to help their marginalized students succeed?

John: Colleges have done a lot to recruit a more diverse student base, and we know that education can be a really effective way of escaping from poverty, but has that been as successful for the marginalized students that you’ve mentioned as it had been for earlier cohorts of students?

David: Absolutely not, no. So we still have really striking gaps, certainly the sort of more famous gaps that we know are between white students and black and brown students, where the gaps in graduation rate are essentially stagnant for the past 30 years, but similarly stubborn gaps exist between well off students and economically insecure students. If you look at students from the top family income quartile, 69% of them graduate within six years. That number for the lowest quartile is 26%. First-gen students are twice as likely to drop out within the first three years as their continuing generation peers. These are things that we know about, but there’s also a lot of research that suggests, not only are we not closing these gaps, we may be making them worse, which is kind of shocking to think about, but there are studies that look at the performance of marginalized students and control for pre-college characteristics like high school quality or pre-college academic performance, and find that they say low-income students still perform worse than you would expect and perform worse than their more well off peers. So I still think there’s lots that we can do, and it’s not for lack of trying, but it is, I think, lack of trying to influence what happens in classrooms.

Rebecca: So you talk a little bit about marginalized students entering into college, and we also know that a lot of these students have a lot of social inequities that impact them before they arrive at our institutions. The focus of your book, though, is, of course, on what happens in the classroom. Can you talk a little bit about why you decided to focus on in the classroom instead of what happens before?

David: Yeah, so part of that is that point that I just made, that I do think that there are things that a lot of colleges are doing that are actually making inequities worse, but the main reason is that in my work, I write to faculty members, I write to professors, and so my focus is really trying to be practical. What can we do? It would not make sense for me to advise professors to put their focus on housing policy or put their focus on ending racism. We know that most of the inequality that we confront in our classrooms comes from far afield of our classrooms, that our students are coming to us from really disparate backgrounds. They have all sorts of things going on in their lives, and most of the causes of the inequality that we see we can’t do anything about. And so that’s really dispiriting. So I wanted to sort of look at, well, what can we do? What are the things that we can change in how we teach that might actually change the trajectory of our students? I do think that most professors are hoping to transform their students’ lives, even if that’s a sort of modest transformation, of transforming the way they see their subject, or something more capacious, of transforming the course of their lives. I do think that we’re hoping to change our students for the better, and so thinking of, well, we can’t get to the root of these problems, but there are things that we can do. I think that really helped me sort of focus on what to write about and what to promote in the book.

Rebecca: I know it can be really frustrating to faculty to always talk at a higher level of like, what are all the underlying causes, but having something actionable [LAUGHTER] is really helpful.

David: Exactly.

John: And when we have students in our classroom, we have to deal with the students we have. We can’t go back and undo their past experiences with education and inequality and so forth. So we don’t want to ignore the students that we’re currently dealing with. And in the first chapter of your book, you call it imaginary students and imaginary classrooms. What are some of the ways in which faculty preconceptions differ from the reality they experience in classrooms, and what causes this disconnect between faculty expectations and the actual reality they’re working with

David: The broad strokes of the imaginary students problem, as I think of it, is that most of us imagine our students to be more privileged than they are. That’s the sort of broad thing. There’s lots of specifics into that, but generally, our students are worse off economically, aren’t as white as we think they are, or will be before we meet them, have all sorts of aspects of their identity that make their path through life trickier than what we imagine they’ll be. There’s lots of reasons for this. One of the sort of more, I think, persuasive things that I came across in trying to figure out why we have this kind of disconnect is the idea that within the sort of hierarchy of universities and what we think of as university quality, but maybe we should better think of it as university prestige. Almost every professor in this country teaches at an institution lower on the totem pole than where they were trained. It’s the nature of our industry that you get a PhD, and then you end up getting a job, usually somewhere with less prestige than your graduate institution. Only a small proportion of American colleges and universities offer doctorates. This is one part of it. What this means is that to the extent that professors are trained how to teach, they’re trained on a more privileged sub-population of college students than the ones they actually teach. So that’s one big issue. Another big issue is that for the most part, we don’t train professors to teach. This was, of course, the subject of my last book, The Missing Course. And so what this means, practically, is that I teach very much like my professors taught, because I learned by watching them, and they, of course, teach by the way their professors taught, but my professors taught a student body that was far more privileged than our student body. Now I think it’s easy to forget the sort of diversification of the American higher education system is actually a really recent phenomenon, as late as 1990, which…I don’t know about you, but I remember 1990… 8 out of 10 American college students were white. Right now, that’s 55% and dropping. And so the sort of our sense of what a college student is and what a college student can do is sort of based on an earlier version of who college students were. And so this comes out in all sorts of ways. One of the sort of more visible ways that we see this disconnect is in the amount of time we expect students to be able to put to our classes. And I can sort of feel every faculty member that’s listening to this nod about this. Because everyone is concerned about students not reading as much, not devoting as much time to their studies as possible. My students work 20, 30, sometimes 40 hours a week on top of being full-time students. An astonishing number of my students take seven courses a semester. Many of them have family members they take care of, many of them actually volunteer. They’re very, very busy. And when we have surveys of how students spend their time, typically, although we expect them to spend two hours outside of class for every one credit hour in class, the surveys suggest that it’s about half that that students spend, on average, about one hour outside of class doing schoolwork for every credit hour they take. Now I’m not suggesting that we lower our standards or even that we assign less work, but we’re not going to help our students succeed if we don’t acknowledge the reality that they don’t look like what we expect them to. So that’s one of the more visible ways that our students are kind of having more difficult lives, to put it bluntly, than what we may expect them to have.

Rebecca: Can you talk a little bit about or maybe provide an example of a way that a faculty member can support students that have all these tugs on their time and attention?

David: Yeah, well, a couple of things. The first thing that I always try to do is acknowledge it. I do think that we can actually achieve a lot by letting students know that we understand that we are one important thing among many in their lives, and if we don’t treat them like our thing is the most important thing, and they have nothing else weighing on their time, we can sort of get to a better level of communication with them, where I can make the case to them that doing work for my class is going to be valuable to them. So that’s the sort of the case, or the psychological approach you can take. The other thing that I think is, like the easiest thing to do, it’s not that easy, is really focus on taking advantage of class time. Like, I don’t know what many of my students do outside of the classroom. That is their lives. That is their time. And they may have quite different and unequal circumstances affecting the amount of time and attention they can give to the work that I want them to do. I do know that they have to come to class, and so this isn’t me saying get rid of homework, but I’m much more aware of using class time to have students do the work that I want them to do. I no longer think it’s a responsible thing to just lecture at students and give them information. I want to take advantage of them being together, of being in a social environment where they can do work with my guidance and with the feedback of myself and their peers. So that’s one thing that I really think about when thinking about the inequalities of time and attention that we see.

John: Marginalized students often experience some degree of imposter syndrome or stereotype threat. What are some strategies we can do to break that down to create a better sense of belonging for all of our students?

David: Sure, this is one of the most well established psychological barriers that anyone that faces in academic domains, stereotype threat, the sort of concern that you may be the target of a negative stereotype, consumes part of the brain and takes away cognitive resources from the important intellectual work we hope students will do. So, one of the sort of most effective broad strategies we can put into place to combat stereotype threat is inviting students to share a broader part of their lives in our classes. One of the things that we know is that students who suffer from or who are targets of stereotypes around academic performances, those stereotypes can be mitigated if students feel that they’re not just their academic selves in the class. If students are reminded that they are not just students, but sisters and brothers and children of parents, that they are friends, that they are soccer players, that they are interested in crocheting, whatever it is, they’ll have a broader base of self integrity, of self confidence to fend off stereotypes. It’s not that they’re going to stop understanding that they might be stereotyped. It’s going to have less power because they’re not just going to see themselves as students within their classes. And a lot of how students see themselves within our classes is based on how they think we see them. So if they believe that we as professors see them as more than just their academic performance, they’ll be more likely to have that broad base to work from. So part of that is conveying an understanding that students have a lot going on in their lives, but also there’s lots of little things you can do to invite students to share parts of their lives in class. I do this thing that I know a lot of professors do, that I call question roll, where to start the class. I always ask students a question, and everyone has to answer it. And for me, this question almost always has nothing to do with what we’re going to talk about in class, and it is a no stakes, no wrong answers. Type of question I’ll ask, what’s the first thing you wanted to be when you grew up as a kid? What’s the best thing to eat for lunch in Iowa City? What’s the best fruit? There’s a lot of food related ones, but it’s a way to ease students into class, get them talking before there’s any stakes, but also, in a kind of subtle way, it allows them to share who they are, without them even sort of realizing that that’s what they’re doing. And what I’ll find is that I start every class with this, and that if I ever forget to do it, my students are completely up in arms and like they cannot imagine starting class without doing question roll if I didn’t ask my question. So they really get into it, even though at the beginning of the semester, they’ll think it’s silly and they’ll resist answering questions. It kind of lays down that precedent that in this class, yes, we’re going to do hard work. Yes, we’re going to try to understand difficult concepts, but we’re all people coming from various places and that we’re going to have a more successful time if we don’t deny those parts of ourselves. So I really sort of believe in this sort of broad strategy of trying to invite students in, invite more parts of students. That, to me, is the kind of subtle thing, or a broad thing that can show itself in lots of different ways throughout a semester, but I do think that if we can think about part of our job as inviting students to share parts of their selves that may not be obvious to them as relevant to their academic selves, that we can really help to mitigate stereotype threat for them.

Rebecca: One of the things that we’ve talked about on many episodes of Tea for Teaching is growth mindset, and that’s something that I know you cover in your book as well. Can you discuss some strategies that help nurture a growth mindset in our students?

David: Sure, one of the more interesting things I learned in doing the work for this book, I of course, had read a lot about growth mindset, and I’m a parent, so I understand all the research into the encouragement of a growth mindset in individuals, but one of the things that I saw is that a lot of the big studies that look at the effects of promoting a growth mindset in the students, the results aren’t as good when the environment students are in work against the growth mindset. So when their peers have a fixed mindset, and/or when there’s a school culture that has a fixed mindset, any promotion of a growth mindset within an individual is kind of blunted, which makes a lot of sense to me. And so what I hope to do in the chapter I have on growth mindset is move away from the idea of growth mindset as sort of teaching students that their brain is a muscle. I think there’s a kind of limitation to sort of telling students to have a growth mindset, and we should focus more on the signals we send to students about the environment of our classrooms. That much more important is looking closely at the way that we do things in our courses and asking ourselves, “Is this focused on student growth, or is this focused on sorting out the better students from the worst students?” So I think about how we talk to students in our syllabi, how we literally talk to students in classes. Are we focused on persuading them that we’re working for them, that the course is designed to help them grow, rather than persuading them the course is going to root out the people who don’t have it, or look to your left, look to your right, one of you won’t be here, or even sort of the purposes of assessment. If we can explain to students that assessment is really important for teachers to understand how students are doing so that we can help them better. Students generally assume, and a lot of professors talk about it like this, that assessment is to sort out whether they know it or not, to give them a grade. And of course, that’s part of what we do, but to me, that takes a back seat to the formative aspect of assessment. The main purpose of assessment, for me, is to see how students are doing, because I want to help them get better. So those kind of more sort of environmental aspects of a class, I think, make a bigger difference in promoting growth mindset than talking about it directly and promoting it to students a sort of individual version of that, one of the sort of strategies that I think goes along with this idea of a growth mindset culture, is something called wise feedback. Came from research by Jeffrey Cohen and his colleagues that looks at particularly how we give feedback to students on their work. They looked at student writing, but I think it can be for any kind of student work. And essentially, wise feedback has three aspects, where you start by invoking high standards. You sort of say, I’m giving you this feedback because I have very high standards for you, either because of this class is really difficult and hard and I’m trying to bring you to a high level, or because I believe that you’re capable of meeting these things, following that invocation of high standards always has to be encouragement. So I’m giving this feedback because I have very high standards, and I know that you can get there, that you can work on it, and then you sort of fill out that encouragement, or you prove that encouragement by giving specific guidance as to what students can do. So most of us, I worry, have been sort of taught by experience and trained to give feedback to justify our grade. I think of particularly the early career instructors I work with, who are very concerned with, for good reason, being challenged about grades, and so their feedback they give to students on paper, essentially, is, here’s why I’m giving you the B minus. Instead, if we think about this kind of growth mindset approach to feedback, here’s where I think you can get, here’s why I believe you can do it, here’s how you can get there. I think that’s a much more effective way to get feedback, where you’re actually teaching through your feedback.

John: You just noted that one of the issues is that it’s not just something you can do in your class with students or encourage them to have a growth mindset if that’s not what they’re observing, and one of the studies you cite is a study done by Elizabeth Canning and colleagues, who looked at the impact of faculty mindset on student learning outcomes. Could you talk a little bit about that study?

David: An amazing study they looked at science professors at a big midwest University, and I think they surveyed all these faculty members about their mindsets, not their students mindsets, but the faculty members mindsets. And they found that the faculty members with a fixed mindset had much larger achievement gaps in their classes than the faculty mindsets with a growth mindset. This was independent of faculty identity, by the way. So this was even the case for faculty with marginalized identities themselves. So it seems that, at least as I interpret it, that our students are taking cues from us a lot of the time about what’s expected of them and what the course is about. And so if we have a growth mindset, if we come at the world with a sort of theory of mind that people can improve, that people can get better with practice. Our students will notice that, and they’ll take what we tell them in a constructive way, in a way that they’ll actually work at something. If they suspect from the way we talk, from the way we carry ourselves, from the way we structure our courses, that we actually have a fixed mindset, that we believe that generally talent is doled out somewhere, and either you have it or you don’t, they’ll get the message that when they struggle early on in a semester, that there’s essentially not much they can do. And so I do think it really matters how we talk about our subjects, classes, how we talk about our disciplines, how we talk about what student work is for. There’s been really interesting research done on the concept of brilliance, and certain disciplines value brilliance in their fields. Philosophy is one that comes to mind a lot, that if you survey faculty members about what it takes to succeed in this field, philosophy and some other fields, for sure, rank being brilliant really high. You need to really be smart to do well in this class. Well, research has shown that those disciplines that value brilliance most highly have the biggest gaps between privileged students and marginalized students in their fields; they have the fewest black PhDs, for example, the fewest women PhDs. And so how we think about what it takes to succeed in our field really matters to students, and whether we tell them it explicitly or not, it comes across to them.

Rebecca: So we’ve talked about a few different challenges related to inequity that our students face, and your book explores many others. Can you talk about a few more examples of strategies that can be used to promote more equity and student success?

David: One of the sort of best supported strategies for what certainly used to be called inclusive teaching, I don’t know if people still use that phrase, is structure. There were a couple of really huge, really sort of ironclad surveys about the inclusion of structure into large lecture classes, and how that helped to narrow gaps that were really, really stubborn, and nobody seems to know what structure is, so I’ll explain it a little bit. Structure in the context of educational research is really just having students do stuff. It’s the opposite of a lecture, where all students have to do is sit and listen to a lecture. Structure is where you make students answer questions before the lecture, where you put them in groups and talk about the concept, where you do a survey in class and have students respond to it, where you have students do writing in class. It’s structuring class time so that students have things to do for their learning. And so we know that that is really effective, particularly for marginalized students. One of the reasons why this helps is because privileged students need less from us than marginalized students. One thing that privileged students have that marginalized students don’t have is a positive experience with school. They like school much more than marginalized students do. They’ve had a good experience. They know how to be good students… I’m speaking very broadly, of course… but on the whole, and they have more resources to help them with that. And so a class period where all students have to do is sit and take notes and do the work themselves, is going to favor those students who are good at being students, and it’s going to disadvantage those students who have had a less positive experience with school, who have fewer resources to help them out, with less time to do the work. So the more that we can structure class time is what I was talking about, taking advantage of class time, the more that we can help those students. I’ve also sort of been convinced that the best kind of structure is transparent and predictable. Transparency really helps students for lots of reasons. I think part of it is that growth mindset piece, that if we explain to students why we’re doing what we’re doing, we will most likely persuade them that what we’re doing is to help them learn, which we can’t take for granted that they believe. And the predictability piece, I think, is really interesting. There’s been really, I think, provocative and powerful research done on the value of taking high-stakes exams, and instead of giving, say, two or three exams over the course of semester, dividing up the points you’d give to that and giving students weekly exams, weekly quizzes, and where you ask about the same material, you just do it more often. This strategy seems to really help economically insecure students, and there’s some sort of disagreement as to why this helps, but one of the reasons why I think it helps is that economically insecure students are used to and good at focusing on pressing concerns. We know a lot about how economically insecure students struggle with long-term planning. What they’re good at is short-term planning. Living in near poverty means that you have to focus on pressing needs, because it’s a life of instability and scarcity. And so if the task is study for this quiz on Thursday rather than study for an exam next month, we’re going to help those students who have fewer resources to do that kind of long-term planning, the long term thinking that is involved in preparing for a midterm or an exam in a month or two. So thinking of our structure as, yes, having students do stuff, but wherever possible, make it transparent, so that students always know why we’re asking them to do the work we’re asking them to do, and if possible, predictable, so students can sort of count on our classes as being a reliable source of support and help in a world that is less than reliable for them.

John: So one of the things your book addresses that’s often ignored in many books about effective teaching is a focus on student disability. Can you talk a little bit about this chapter and some recommendations on what institutions and what faculty can do to help create a more supportive environment for students with disabilities?

David: It’s one of my favorite parts of the book, partly because I learned a lot, and so we know that disabled students struggle in our institutions, that they graduate at a much lower rate than their able bodied peers, and that the numbers we do have about their success are probably under selling the problem, because something close to a half to 1/3 of students with disabilities actually report their disability to their institution. So we know that plenty of students, even plenty of students who get institutional support in high school, aren’t telling their institutions in college, and so aren’t getting the support they need, and aren’t getting recorded in these studies. What I did in the course of my research was I kind of looked at the history of disabled students in the academy, and I found, probably unsurprisingly, that the Academy had to be sort of dragged kicking and screaming to care of and educate disabled students. It was only by force of law in the 1970s that American institutions of higher education began admitting disabled students in large numbers and admitting them into mainstream classrooms. But going over that history, which was of course, also the history of the creation of Student Disability Services offices and the accommodation system really made me think of how we take for granted this system that we really shouldn’t accept as being the way things should be. Essentially American colleges and universities were told that there was this new population of students with different needs coming into the academy, and that they had a mandate to give them as good an education as everybody else, and that was an opportunity to tell professors in particular, look, you’re going to get a lot of new students with different needs. Maybe you should change the way you teach. Maybe you should change the way that you approach your students, because your students are going to have some different needs. We didn’t do that. We didn’t do that at all. We didn’t touch the center of our institutions at all, in terms of what I think of as the center of our institutions, our classrooms. Instead, we created an office on the margins of our institutions, and a system that too often allows professors to change very little about how they teach. In fact, seems designed to allow you and I to change very little about how we teach, and even if we look at the language that universities use around accommodations, it very much is about students have a right to an accommodation, but only if it doesn’t interrupt the educational standards, are very often the way they’re phrased, and I just don’t understand what educational standards could be if they’re not helping all of our students learn. And so this idea that we can help certain students, but only if it doesn’t interfere with some vague sense of standard that is unconnected with helping our students that I just don’t understand. And so I really want faculty members to sort of rethink how they approach a class before they meet their students. We very often think of our disabled students as an exception, as something that comes after the fact. We meet our students, we tell them the syllabus, we tell them the way it’s going to be, and then you start to meet some disabled students who say, by the way, I have an accommodation request, and you make an exception for them. So I’m very much drawn to, of course, Universal Design for Learning, which is, of course, a long time, broad approach to teaching, it comes from many disability activists, that seeks to, before we meet our students, design our classes so they’ll work for everybody. This is an ideal. It’s not easy to do, but it’s the right approach, rather than pretending that all of our students are able bodied and then being surprised every time that we get X number of disabled students asking for a change. So I go into a lot more detail in the book, but that’s one of the reasons why I love writing despite how much I hate writing, is that sometimes you can learn something. Is that actually doing this research really opened my eyes to kind of what we take for granted about the place of disabled students on campus and how the system seems set up to cast them as exceptions. In my opinion, that underlies a lot of their struggles to succeed.

Rebecca: It’ll be interesting to see how the updated regulations to Title II of the ADA, which addresses public institutions, helps move this work along, because it does require this proactive approach around access, especially around digital technology.

David: Yeah, I don’t have high hopes, but I do have hopes.

Rebecca: So we focus a lot on in-person classes. Can you talk a little bit about the relationship of the contents of your book to online classes.

David: The reason why I mostly address in-person classes is because that’s most of my experience. I am skeptical of a lot of online education. I know that there’s a lot of people who’ve done really great work, and there’s a sense in which sometimes that online educators have had to focus on some of these issues before people in person have had to, particularly around generating good community, where you sort of have to work harder. I do hope that all the principles that I advance will work in sort of any classroom. I just think that we do faculty and students a disservice if we pretend that online education isn’t harder. I think it’s harder. I do think that we are doing people work, care work, and that good teaching has to do with forming relationships with our students and helping them as human beings. I used to have this joke when I worked for a teaching center when people would come in for advice about their teaching. I’d say half jokingly, all right, I want you to imagine that this is your student, and you’re their teacher, and you’re trying to help them. I do think that sometimes we sort of get away from the idea that these are people that we have relationships with, they’re professional relationships, they’re educational relationships, they’re relationships. I think that that relationship is harder to see in an online environment, at least it is for me, and so it takes more work to nurture and make work. It certainly can work, but it’s harder.

Rebecca: Challenging, both for the students and for the faculty. [LAUGHTER] It goes both ways.

John: We always end with the question: what’s next?

David: Well, what’s next for me is, I’ve just been recently made Director of Programming at Iowa called Gen Ed. Lit is what we call ti, general education literature. It’s a class offered by the English department that every undergrad at Iowa, except for English majors, has to take. So about 3000 students every year take it, 150 sections, something like 60 or 70 instructors. Most of them are graduate instructors, and my role is I oversee this course, and I’m a supervisor of all of these instructors. So I took the position because I’m really passionate about training new instructors and helping people learn how to teach better. And so for me, it’s an opportunity to put a lot of the ideas that I’ve been thinking about and writing about for years into practice with people who are actually on the front lines, which is a terrible metaphor, but I’ll use it anyway. But teaching our undergrads, and in particular in this course, teaching students who, for the most part, haven’t chosen to be in the class, which is, as you know, one of the more challenging teaching challenges that we face. And so I’m really excited about helping new instructors find their feet and helping figure out, particularly at this time, where everyone is worried about AI, helping to figure out how to teach literary reading skills to a generation that I think could really benefit from developing them.

John: And a generation that spends much less time reading than prior generations. So it’s a nice challenge to be addressing and working on it, and I hope it’s successful, and maybe, if so, you can share that more broadly so we can all improve this.

Rebecca: Yeah, definitely sounds like a challenge that might keep you busy for a little bit anyway. [LAUGHTER] Well, thanks again for joining us and sharing your work.

David: Well, it’s been my pleasure. Thank you so much for the work that you do and for talking with me today.

John: Thank you. We’re looking forward to future conversations with you.

[MUSIC]

John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing assistance provided by Madison Lee.

[MUSIC]