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The Transform your Teaching podcast is a service of the Center for Teaching and Learning at Cedarville University in Cedarville, Ohio. Join Dr. Rob McDole and Dr. Jared Pyles as they seek to inspire higher education faculty to adopt innovative teaching and learning practices.
If I tell you to do, like, one very specific narrow thing, the only way I'll do it is if I like you or if there are consequences if I don't. But if you give me a bunch of choices, then I'll choose the one that I just wanna do. You create sort of this space where people can breathe. And I think that's that's what anyone can do, pretty pretty quickly, just looking at what they're asking of their students.
Narrator:This is the Transform Your Teaching Podcast. The Transform Your Teaching Podcast is a service of the Center for Teaching and Learning at Cedarville University in Cedarville, Ohio.
Ryan:Hello, and welcome to this episode of Transform Your Teaching. In today's episode, Dr. Rob McDole and Dr. Jared Pyles chat with Doctor. Stephen Aguilar.
Ryan:He is an associate professor of education in the educational psychology concentration at the USC Rosier School of Education. In the episode, they discuss the intersection of technology and motivation. Thanks for joining us.
Jared:Rob, we are continuing our series on stirring motivation. Our guest today is, also a professor at USC, just like when we talked with, Erica Patel.
Stephen Aguilar:Mhmm.
Jared:So, why don't we let our guest give us a quick bio of who they are, and then we'll roll with it. Go ahead. Give us a give us a quick bio, sir.
Stephen Aguilar:Sure thing. My name is Stephen Aguilar. I am an associate professor of education at the USC Roswell School of Education. I'm also the associate director of the USC Center for Gender of AI and Society. I I study how motivation and technology intersect, and right now, a lot of that has to do with AI.
Stephen Aguilar:So happy to get into it.
Rob:What really got you into the psychology of motivation and self regulated learning? Can you speak to that real quickly?
Stephen Aguilar:Yeah. So I am interested in why people do things, and and particularly why students even bother. Right? Learning is hard. And and if you think about anytime you wanna learn something, you're extending yourself or the unknown.
Stephen Aguilar:Don't know if you can do it, and that requires a little bit of bravery on your part as a learner. And but it also sort of signals your willingness to push beyond the boundary. And that doesn't happen by itself. Right? That's not just you deciding to do something.
Stephen Aguilar:You go into an environment that pushes you in those ways. And we often think of classrooms as those environments because we we sort of built entire systems to to help students do that. But, you know, learning happens everywhere. And and I was I've I've always been interested in how do we design environments to support that journey of moving from I don't know how to do something to I kind of know how to do it, to I actually know how to do it and can enact that behavior moving forward with confidence? Because designed environments shape that entire thing.
Stephen Aguilar:And and before that, you know, I was an instructional designer, so I thought really deeply about how, you know, curricula are designed to to sort of support students in those ways. I was in ed tech for a little bit, so I thought about how systems were designed to support students. And for me, I didn't know enough as in either of those roles to to do exactly what I wanted. So that's when I decided to pursue a PhD in educational psychology with a focus on educational technology.
Jared:So learning analytics, that's kinda where you parked yourself. Tell us about that. Tell us about how that how that can help explain motivation of students.
Stephen Aguilar:Yeah. So but, you know, pre generative generative AI days, institutions, a a lot of colleges and universities were sitting on a lot of data that they just had. You know, student SIS data, you know, who students were. But there were also a lot more uses of, like, learning management systems, dashboards. And all these systems also collected a lot of behavioral data.
Stephen Aguilar:Like, we could see, you know, hypothetically, when students were accessing assignments, etcetera. And during the time where I when I got into this, this is about 2011, 2012, there was a lot of work that was happening on how do we take these sets of data, merge them, and understand how students are learning. And for me, my entry point into that was really trying to have a better understanding of not only can we understand sort of at the basic science level, like what students are clicking on, whether it's associated with the data level with the course. Like, that's all interesting. But I was more interested in the the what we called, like, the translation layer going from the data toward insights.
Stephen Aguilar:Right? If you're, like, in business, you might sort of think of, you know, like, business intelligence or, like, those dashboards that let you know when someone clicked on a thing to buy it. It's that. But in education, it's less clear when learning happens. Right?
Stephen Aguilar:If Amazon knows that you bought something, they know it's a one or a zero. You bought it or you didn't so they can sort of manipulate what's happening. When it comes to learning, the the indicators are are a lot less a lot less clear. And for us, it required us to and for me in in in my early work, I really wanted to understand what sort of information can we give to students to shape how they're under how they understand their own learning journey, how they see themselves as learners. So a lot of that work dealt with, do we show students comparative data?
Stephen Aguilar:Like, here's how you stack up against your peers. Right? You are on this part of the distribution. Like, you can think of if you play video games like a leaderboard. Or do we just show show students how they're doing relative to themselves, like their growth?
Stephen Aguilar:And, you know, really trying to understand how both of those decisions end up influencing how students think of themselves as learners.
Rob:So what did you find?
Stephen Aguilar:I found that both are helpful, but it depends. It it depends on the learner. So a lot of the the theory that I used to guide this work was achievement goal theory. And sort of the the two things that I mentioned were sort of performance orientation. Right?
Stephen Aguilar:Think of that as I want to either be first in a race or I do not want to be last. So those are two different dimensions of thinking about performance. You wanna be the best or you do not want to be the worst. And sort of mastery orientation, which is like, I don't care what's happening around me. I just want to get better.
Stephen Aguilar:And what we found was that generally speaking and this is sort of true of of the literature sort of outside of learning analytics. But when you showed students displays of their own performance over time, that did tend to orient them more towards growth, more towards wanting to improve their own performance over time relative to themselves. Performance orientation, like, it depends on the students. Some students are really motivated by wanting to lead the pack, by wanting to sort of do better than than their peers. And this can can be a good thing when it comes to achievement and when it comes to learning content.
Stephen Aguilar:The downside though is that some students are avoidant. And it's not about being the best. It's just about really trying not to be the worst. And that can sort of cause a number of different behaviors. They could sort of undermine their motivation because they're just they're gonna try just enough to not be last.
Stephen Aguilar:They could cheat. Right? Because if if all I care about is my position within a within a distribution, easiest way to not be last is to cheat. So it so it ends up being pretty complex. But overall, I think that the the dashboard work signaled to the community that we we wanna sort of preserve what we already know about student motivation, which is if we can encourage students taking mastery perspectives, that tends to be better.
Stephen Aguilar:It tends to be sort of more broadly applicable.
Jared:So you're talking dashboard wise. Help me think through, like, in our LMS with Canvas, are you talking something outside of just the normal grade view that students would see? Is this something that an instructor would build themselves? Or like, what I'm trying to think practically if Sure. If someone wanted to create a dashboard like this, what could they use?
Jared:Yeah.
Stephen Aguilar:So this was before, like, learning management system companies really started to do their own analytics to start to build their own dashboards. So I I was at the University of Michigan, and if you've ever sort of had spent any time at the University of Michigan, Michigan likes building its own stuff. Like, it off it doesn't always, like, buy stuff from vendors. So it was it was an interesting experience because a lot of what we we what we did was sort of built in house. So what we what we really wanted to do was do that.
Stephen Aguilar:Right? To which dashboards make sense given the information that we have, given grade information? Do we do we have granular grade information that that exists within an LMS? And, you know, we wanted to for me, it was almost more about understanding sort of big picture things and coming up with design guidelines that you could sort of take wherever to so that folks could have a better understanding of what sort of information is helpful for students to see what sort of information is generally not as helpful for students to see. Or we think it's helpful, but it actually isn't.
Stephen Aguilar:Right? There there was this philosophy of let's give students all of their information because it's going to just, you know they're better informed. They can make decisions. That's not necessarily true. Right?
Stephen Aguilar:If I Sure. Tell you that you're last, then you're just gonna feel bad. It's not gonna help us sell you.
Rob:Well, I mean, there's always the possibility of somebody going, oh, I'm not gonna be last much longer. But you're right. The reality is it's gonna make them feel awful.
Jared:Well, you mentioned it also that depends on the student too. Yeah. Like, you could have those that are super motivated by that leaderboard idea. Then you have others who are like, you know what? I'm comfortable just existing in the crowd in the middle.
Jared:No one can pick me out. I don't get any special attention from the instructor. I'm happy in this 75 c range.
Rob:Like the Bengals and the Reds.
Jared:Like the bing oh, that's awful. You know what? The Reds are anyway, thanks for that. Why don't you take the next one? I gotta take a I gotta take a
Rob:moment here. Gotta take a moment here. Oh, okay. So you've done a really good job of giving us an understanding of your foray into analytics as well as motivation now with AI. And I know you've been doing some work in there and I think you're writing a book on it as well, what kind of transformations are you seeing?
Rob:What things are on the horizon in terms of figuring this out as we transition to new technologies that are really having a large effect on on the teaching and learning landscape and in motivation.
Stephen Aguilar:Yeah. You're right. There there's a lot going on. And a part of the, you know, a part of the the the trick is is try to get a a broad understanding of what's possible. Right?
Stephen Aguilar:So so the book that I've written, which comes out August 6, is authenticating intelligence preventing AI from hijacking education, which is a prerogative title. And I actually don't despite that title, I I I sit in the middle. Right? I I I talk about sort of two groups of people. There are sort of AI alarmists that think AI is going to break education because it's gonna undermine everything that we care about.
Stephen Aguilar:And then there's sort of AI zealots who think AI is the solution to all of our problems within education. And, you know, obviously, both both sets of people are wrong. But they're wrong for very specific reasons. And I think that they're both interestingly sort of faith based positions where they just believe that they're correct because of some underlying value system that they express. But when I think about what's possible with with generative AI and education, I I I liken it almost to the emergence of a new utility.
Stephen Aguilar:Right? If you think about electricity, if you think about other utilities that you use, it's this medium that allows us to do a lot of things and a lot of things that are different. Right? So you you don't necessarily, like, think of electricity as, like, a thing that you sort of wield. It powers a lot of things.
Stephen Aguilar:Right? It powers your phone. It powers your computer. It powers your lights. It's this substrate that ends up enabling a lot of different things.
Stephen Aguilar:AI currently and I'm thinking specifically when I say this sort of large language models and a lot of generative AI technologies are similar in kind to to how we should think about what's possible. So if you sort of click out of AI as a as a sort of singular tool
Rob:Mhmm.
Stephen Aguilar:And you instead think about it as something that empowers a lot of different tools, then you can be more discerning about which tools what what does the tool actually do. Right? So you can start thinking about the affordances of a tool. And that's something that I get into a lot in the book, which is when you when there's some sort of AI technology, your first question is, well, what what can it actually do? What do what do folks say it can do?
Stephen Aguilar:What can it actually do? Mhmm. And does what it and does that sort of help me solve the problem I'm trying to solve right in this moment? So to the more directly answer your question, I think that a lot of things are possible. Right?
Stephen Aguilar:More personalization, like of reading levels, for example. That's a that's a pretty clear cut use case where if I have a text that's written at the twelfth grade level, I can scale it. Right? Right. And I can sort of and that's that's you know, I would have loved that when I was a seventh grade teacher to be able to to to to give my kids basically the same text but at their level of access.
Stephen Aguilar:Yeah. But, you know, I don't necessarily want my students to use that as sort of their copilot, if you will, that sort of helps them write because I actually want them to struggle. I want them to write poorly because it's through writing poorly that you begin to crystallize your own thinking and then eventually write better. So sort of accelerating that process through generative AI can actually cause more problems downstream than we'd like to than than we think. So so but again, those are two different use cases, all sort of powered by that same underlying technology.
Rob:What other use cases are you seeing in terms of things that might be helping to motivate students to not say, oh, I don't need my teachers anymore. I don't need, you know, the university that I'm at. I I'm motivated to actually go deeper, learn more, master it, as as you said earlier. Just try towards mastery.
Stephen Aguilar:Yeah. I think that it's this double edged sword. Right? AI is seductive because it or because it's so useful. Mhmm.
Stephen Aguilar:Right? I can pose a question. I can immediately get feedback on it. So is it the use case you mentioned where you have sort of this Socratic dialogue potentially with with a with an agent or with a sort of an AI chatbot, you know, pick the one that that you're that you would like to use. Ends up being the sort of can be this interesting exercise of helping you reflect and sort of be more metacognitive about your own practice or your own writing.
Stephen Aguilar:It obviously comes with limitations. Right? Because one of one of the things that I think that every student and and everyone who uses generative AI should really think about is it does sort of pull you towards sort of that mean. Right? Because that's that's how LLMs are structured.
Stephen Aguilar:Right? That they they homogenize language in ways that can be problematic. Right? So if we think about a a a, like, the act of of writing can be sort of this act of discovering your voice as a writer. And and, you know, obviously, those who write fiction sort of have a know what that means.
Stephen Aguilar:But even if you write nonfiction, you write in a certain way that expresses who you are. Right? So if you keep sort of pulling that towards homogeneity that the NLM recommends, then you do lose a bit of yourself. And sometimes that's okay. Right?
Stephen Aguilar:If I'm writing an email to my colleagues, it doesn't need to sound like me. It just needs to be coherent because I don't wanna, you know, I don't wanna spend, like, fifteen minutes writing an email. Yeah. But if I'm writing something that's for a course, if I'm writing something that that I actually care about, then I should preserve that part of myself within the writing even if it is, you know, slightly incorrect. Right?
Stephen Aguilar:If there's sort of these flaws in it. Right? Because to be human is to be flawed in a lot of ways, so we can't strip all that away without losing a lot of ourselves. But another use case that I think is really can be really motivating for students is ideation and rapid prototyping. So, you you know, we we talk about vibe coding, and I know that folks have a lot of feelings about it.
Stephen Aguilar:I have feelings about it. But the reality is is that I can prompt my way into creating a thing that I thought was interesting even if it's ephemeral. Right? So so one of the one of the more powerful use cases I think is for everyone, teachers, students, anyone who just is interested in understanding something, into doing something. It's to basically build these tiny tools that are ephemeral, that solve a specific problem for a specific time point in time, and then, you know, it goes away.
Stephen Aguilar:Right? So an example I have of this is in in one of the courses I teach at USC, we had this LDT AI program. That's a master's program that sort of helps folks interested in design learn more about how they can use AI. You know, I had students rapidly prototype just things where they had students who were the the basically, the prompt was my prompt to them was build something that will help someone learn. Doesn't matter who they are.
Stephen Aguilar:Right? And so I had a, for example, a pre k teacher or a kindergarten teacher. She just wanted her kids to basically understand drawing shapes, like squares, triangles, etcetera. And it was a very narrow use case. So she built a platform that basically helped to teach her students how to, you know, play with shapes and how to draw them, like, how rapidly iterate on on examples of them.
Stephen Aguilar:That was one use case. I had sort of another student who works for USC basically designed this whole system that you you could feed in syllabi, and it would tell you if if I had sort of an idea for course, it would tell you, well, this that's covered here, here, and here. So you can take these ideas. Here's the here are the gaps that are there. And both of these students would were able to rapidly sort of create this without having a lot of underlying sort of knowledge of coding.
Stephen Aguilar:Mhmm. And I think that is very motivating to them because they could sort of get them to think about their own whether or they wanna go down that path of creating a product. But more to the point, I I I think it's sort of it lets you enact ideas and build tools. And that's you know, humans love tool building. Like, whether or not you you think about it, you use tools every day.
Stephen Aguilar:Sometimes if you have a problem, you're like, oh, I wish I could do x if I just had this thing. It's easier to build that thing now. Yeah. And I think that that can really help people.
Rob:You built some tools.
Jared:I I I did build some tools. I I was gonna say that what you said rings true for me because I have been wanting to build a one of those family dashboard calendars at my home. And I kept seeing all the ads for these $600 computer monitors that have a backend. And I'm like, I could do that myself. But, I didn't have the coding experience that I needed to.
Jared:And so I went and went into Codex and said, here's what I need to build. And then over time I had a one point zero, then I was like, let's make a little bit better. Then I went to two point zero and a three point zero, and now I have an F1 module to tell me the next race and the results and all that stuff. We're flying. And it's what was great about that.
Jared:You talk about motivation and this is something that Rob said to me after I was talking to him about it, that he's never heard me say before was like, now I want to know what I can do next, which is something I would never even think about doing before, but it just took that. I mean, it was difficult at first because I had no experience in that. It was a bit steep of a of a or a heavy lift at the beginning. But, man, I'm a tell you, like, what you said is absolutely true. It was it's very motivating.
Stephen Aguilar:Yeah. No. And and it a 100%. Like, that's that's a sort of a a great sort of story about what's possible now. And and I you know, back in the day, not that long ago actually, but but in my earlier, like, graduate student training, one of the things I I I really focused in on was understanding, like, video games as a metaphor for learning environments.
Stephen Aguilar:Right? There's a reason that video games are engaging. There are these play spaces that give you immediate feedback. You can play in them without consequence. And, you know, you can, you know, for you you can just think of any any basic game.
Stephen Aguilar:Like, everyone's familiar with Super Mario Brothers. Right? You get this many lives. If he dies, well, don't fall in that hole. And then you can you can keep basically doing things until you get better at the game.
Stephen Aguilar:Mhmm. And now that process, that that's engaging because of just a lot of different reasons. Right? The the consequences aren't that dire if you fail. You get lots of different opportunities.
Stephen Aguilar:You can build mastery over time. Right? And and even those three things that I said, you can sort of see how they map onto your experience in in coding this personal project. Right? You can iterate quickly.
Stephen Aguilar:You can get immediate feedback. And that I think is one of the key things. Because back in the day, if I wanted to learn how to code a thing, I could get halfway through, then I'd spend $20.30 minutes, or two hours on Stack Overflow or on something, find kind of a solution, get frustrated, abandon the project, and then come back to it six months later. Right? So now you can at least get you can get so much farther because the answers you get, even if they're, like, not a 100% correct, they get you closer, and you can interrogate those answers to your point.
Stephen Aguilar:You can ask questions. Yep. Why does this do a thing? Why you know, what what's happening here? Obviously, the challenge is whether, know, it's not and here, I think this is where where people can get hung up.
Stephen Aguilar:Right? Doing that is different than taking an idea to production and to and scaling it because those are different sets of issues. And I think that that's sort of the bridge that that some people don't think don't think about when they're gonna cross it. Right? I'm sure you guys get this too where you get, like, a lot of the emails from people saying, I have this idea for this EdTech product, and here's an app I built.
Stephen Aguilar:And I'm like, that's great. First of all, cool. You and 10 other people just email me this thing. But how are you going to take this to an actual, like, live product that goes to production? I'm not gonna do it for you.
Stephen Aguilar:I'll give you some ideas on why the, you know, the design might be an issue. But but I think that that that sort of that can be sort of why it's important to also think about limitations. It's not like this tool, AI, can solve all of our problems, but it can it can do a lot of really cool things.
Jared:So I I do wanna dovetail off of the feedback idea because you've mentioned it in games again. I I wanna ask you about instructional feedback because that's been part of your research. We had doctor John Hattie on to talk about feedback in this series as well. You mentioned Mario. You mentioned falling down a pit.
Jared:There's your feedback. Okay. I don't wanna do that the next time, so I'll hold a a little bit longer. By the way, you're speaking my language by going into video games. But anyway, let's tie back to that idea.
Jared:Like, does that look for instructors as they're giving feedback to students? Like, how much feedback is too much? Because that's a simple Mario falls down a pit. Okay. I know what to do differently this time.
Jared:Or I run out of time in a castle because I took the wrong path. Translate that into how much feedback is enough and what type of feedback do instructors need to give to their students.
Stephen Aguilar:Yeah. So so this is where it gets where sort of learning design and instructional design can become very powerful tools for teachers and and instructors. The downside is that it can also lead to a lot of bookkeeping. Right? So in a perfect world, your learning environment structure is very similarly to a video game.
Stephen Aguilar:There's lot there's multiple paths to success. Right? You don't necessarily beat a level the same way every single time that you play a video game. So you want students to have multiple opportunities to engage with the content. Right?
Stephen Aguilar:So one of the ways that that when I designed the course that did this, right, some students could write an essay, some students could do a presentation, some students could do, like, like, a video blog, multiple paths of engagement. And the idea here is that you going tying back to learning theory, it gives students a sense of autonomy. Right? This hearkens back to to to self determination theory, which which I know you talked to doctor Patel about. Right?
Stephen Aguilar:You you let folks feel that sense of autonomy in choosing their path. That's gonna motivate them. It's also gonna cause you to grade a bunch of different things. So that that that that that's that's where it gets challenging. Yep.
Stephen Aguilar:But, you know, you could also give students multiple opportunities to fail. Right? It isn't that there's these and and this is very common in in college where you have sort of three assignments. And if you fail one of them, you're gonna get a c. Right?
Stephen Aguilar:If you fail two of them, you're gonna get an f. Doesn't matter how well you did at the at the last one. It's not you know, it's still largely, I think, a bookkeeping issue. We just don't have time to grade a bunch of stuff. I think AI must sort of might help us get towards a place where we can potentially sort of do more of that bookkeeping.
Stephen Aguilar:But when it comes to feedback, the idea is to give students multiple checkpoints with you. Right? So you want potentially, if it's an essay, multiple drafts that you're reviewing, they're giving the feedback on, those drafts are lower stakes where you wouldn't necessarily, like, grade them, but you would give candid feedback so a student can improve in this sort of quote, unquote safe space of learning. Right? Where it's not that you're going to get an f here or you're going to get an a.
Stephen Aguilar:You're just going to get feedback. Because that's that's how video games function. Right? They just constantly give you feedback. You do a thing, a thing happens to you.
Stephen Aguilar:And that's how we learn about it. And it's not just video games. Right? It happens on with everything. If you're learning to play a sport, you throw a ball.
Stephen Aguilar:It either goes where it's supposed to or it doesn't. That is feedback. And then you would have potentially a coach that tells you, okay. Here's what you're doing incorrectly. So that's that's the that's the the perfect system, I think.
Stephen Aguilar:But it is it doing it well is can be resource intensive. And I think for for instructors finding how many you know, how much of that they can incorporate without, you know, them without really going insane is important. Right? You're not gonna scale this to a lecture of 200 kids. But if you're teaching a seminar of 12, you could probably have a couple of options of assignments.
Stephen Aguilar:It's not gonna really ruin your day to to grade two different things.
Jared:Well, we've really appreciated your time with us, Doctor. Aguilar. If there is one thing that you would tell, our listeners, the instructors that are listening about this idea of it could be the data you're talking about, feedback, motivation. What is something they could put into practice that you would recommend for an instructor that's listening? What's something they could put into practice, let's say, in a week?
Stephen Aguilar:I would say that, look at all of the assessments and all the assignments that that you've written and think of look at them through a lens of choice. What choices do students have within those assignments? Are they narrow or are they broad? And when possible, move toward giving students more choice because that that creates multiple points of engagement. It could just simply be different ways to, know, interact with the assignment.
Stephen Aguilar:It could be different topics within the assignment. But the the more control you give people, generally, the more willing they're they're they're they are to to meet you halfway or to engage with you and sort of do the thing that you're asking of them. If I tell you to do, like, one very specific narrow thing, the only way I'll do it is if I like you or if there are consequences if I don't. But you don't you can but if you give me a bunch of choices, then I'll choose the one that I just wanna do even if I don't like you and even if there are consequences if I don't. Right?
Stephen Aguilar:You you create sort of this space where people can breathe. And I think that's that's what anyone can do pretty pretty quickly just looking at what they're asking of their students.
Jared:That's great. Well, authenticating intelligence, preventing AI from hijacking education comes out August 6, And you have a podcast as well that you just launched in January. Tell us about that a little bit.
Stephen Aguilar:Yeah. So that's you know, namesake is the book, so Authenticating Intelligence, the podcast. I basically just talk to a lot of different folks about, you know, how they are using generative AI in their own practice. Right? I've talked to policymakers.
Stephen Aguilar:I've talked to my dean. I've talked to folks who are interested in in misinformation and and disinformation. So it's this this idea, again, of of treating AI like that medium, like that utility, which means that there are multiple points of engagement, and I really seek to to help others understand how others are engaging with it through that lens.
Jared:Well, doctor Aguilar, again, we're very thankful you were willing to come on with us. We hope to hear from you again, especially we didn't talk about your game based studies that you're doing, the gamification idea. And then we wanna have you back on when the book comes out so we can talk about it after we read it too. So
Stephen Aguilar:That sounds good. Happy happy to do it. Reach out anytime.
Ryan:Sure. Appreciate it. Thanks for listening to this episode of Transform Your Teaching. If you have any questions or comments about our conversation with doctor Aguilar, please send us an email at CTLpodcast@Cedarville.edu. You can also connect with us on LinkedIn, and don't forget to check out our blog at cedarville.edu/focusblog.
Ryan:Thanks for listening.