The future of learning
Candace Thille is an authority in learning science, educational technology, and AI-enabled learning environments.
She is closing the two-way gap between the science of learning research and the hands-on practice of instruction to help students learn better. Timely and targeted feedback with the opportunity to apply that feedback is critical to learning, Thille says, and this is an area where AI supporting humans excels. She imagines a day in the not-too-distant future when human educators and AI-enabled assistants unite to help students learn faster and better than ever before. Learning is not a spectator sport, and AI can help us engage with learners – and educators – in new ways, Thille tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.
Transcript
[00:00:00] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host Russ Altman. Since we started this show eight years ago, it's become an archive of amazing and impactful work by my Stanford colleagues. Research is not something that just happens in the lab, and as you'll hear on this show, the research at Stanford can impact areas like health, technology, law, and business, and many other topics that can affect everyday life. We hope you'll tune in to learn more about how research has the potential to help your life and to help the lives of people you care about in your family and your community.
[00:00:33] Candace Thille: In that computer interface, we can observe the learner. And we can observe the learner, just as you said, like Netflix does, like Amazon does. Uh, they're trying to do it to understand you better as a consumer.
[00:00:46] Russ Altman: Yes. Very different motives.
[00:00:47] Candace Thille: Very different motives. We're trying to do it to understand you better, you individually, as a human learner.
[00:00:59] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. You know, if you've listened to this show more than once, or even just once, and you liked it, go ahead and follow us. That'll help make sure that you hear about every episode and never miss anything about The Future of Everything. Today Candace Thille will tell us that research about learning and practice of teaching and learning need to be tightly linked. If they're separate, you might not get the best results, but if you can integrate them, then you will get a cycle of improved teaching and improved learning. It's the future of learning.
[00:01:33] Today we're continuing our feature the Future In a Minute with Candace. At the end of our interview, I will ask her a few rapid questions and she will give us beautiful short answers. Before we get started, please remember to hit follow. If you listened to us once, if you've listened to us twice, no matter how many times, you probably, I hope, are enjoying us, go ahead and follow. We'll keep you up to date. So we all know that learning is important. It's important for productivity and your functioning in the world, but you know what? Learning also makes people happy. Researchers in learning learn a lot about how to do the best teaching and how to facilitate learning, especially in adults. However, there's a problem.
[00:02:20] Sometimes the research that is done in the academic controlled setting doesn't always transfer well into the real world teaching and learning schools. Well, it turns out that there are ways now where we can integrate the research about learning with the practice of learning, to have a tighter feedback loop and to increase the relevance of the research to the actual practice of learning. This means we have researchers who are producing better and more robust results, and we're having learners who are doing better at learning faster and better. We'll hear about that from Candace Thille, who's a professor of education at Stanford University. She's an expert in media and technologies in the context of learning, and she will tell us how learning knowledge and research has evolved and how AI, at the end, is impacting her ability to build effective learning environments.
[00:03:16] Candace, to start out, why did you decide to focus your career and your research on the science of learning?
[00:03:22] Candace Thille: Oh, because human, because learning and the capacity to learn and having agency in your learning has pretty much every kind of outcome, positive correlation that you can imagine. And I wanted people everywhere to be able to learn what they wanted to learn, to build the knowledge and skills they needed to do what they are trying to do in the world.
[00:03:47] Russ Altman: Great. Great. And so kind of a little bit out of order, can you tell me what is the science of learning and what are the current big challenges to the field?
[00:03:56] Candace Thille: Oh, the science of learning is, first off, it's a really nascent science, unlike physics. It's not like we know all of this stuff, and it's just a matter of getting everyone to know what we know. It is, uh, so the science of learning is studying actually how people learn. Um, that, that moment of where your knowledge and capability changes from one moment to the next. And, and it's an interdisciplinary field. People come at the science of learning from, obviously from education, but also psychology, communication, computer science is huge. Um, neuroscience, all of those are involved in really understanding the processes of human learning.
[00:04:42] Russ Altman: And, and you work at a school of education. Is the science of learning a kind of integrated into the curriculum for, for educators?
[00:04:49] Candace Thille: Um, not for all educators. At our school at Stanford, we have a particular, one of our graduate programs as well as a master's program is called Learning Science Technology and Design. And that is a real focus on both the science of human learning and also, um, how you use emerging technologies and a design science to take what we know about human learning and use it to design effective educational experiences.
[00:05:17] Russ Altman: And so I, I hope this is a fair question, but when you look at how teaching and learning happens in the world, and I think your expertise is adult, adult learners. So like, and I think that includes college, we're gonna call college students, adults for the purposes of this discussion,
[00:05:32] Candace Thille: Over the age of 18.
[00:05:34] Russ Altman: Great. Um, when you look at, um, the, the, the way we instruct them, are we making the best use of our knowledge of, uh, learning science or, or do you see a little gap there?
[00:05:44] Candace Thille: Oh, there's, there's a gap there and everyone will always talk about the gap that you are pointing at, which is, we know a lot about how people learn, but very little of what we know actually influences practice. But I wanna talk about this as a bidirectional gap. There are a lot of people, and I'm, I know most of, most of us have experienced them, that are amazing teachers. And some of them are formal teachers. Some of them are just people who help us learn things, and they actually have a lot of wisdom and knowledge about how to support someone to learn something. And the other gap is all of that knowledge, all of that skill doesn't find its way back into our science. So I call it a bi-directional gap. And my life's work is really shifting that relationship between the science of human learning and teaching practice. Whether that practice is done by a human or by a machine.
[00:06:43] Russ Altman: Great. And that, thank you for that last comment because I wanted to get to the fact that you have embraced technology as, as a, both to, to do this research in your field, but also as a potential way to deliver teaching and learning, uh, as you just, as you just described, and that's not obvious to me. For example, you, I, you, I could imagine a learning specialist saying technology is not really part of this. We're talking about humans inspiring other humans. So how do you think about technology for education and learning?
[00:07:12] Candace Thille: I think about technology as providing infrastructure for human learning, in the sense of, if you think about it, when you're either a learner or when you're a teacher, you're making minute by minute decisions. And your decisions are trying to answer the question, if I'm the teacher for this learner who's in this particular state that they're in, and we're trying to help them to get here, what's the most effective thing I can do for that learner to help them move from where they are to where they're trying to get to? Now in order to, we call those instructional differentiation decisions.
[00:07:51] Russ Altman: Oh, good. That's, it sounds very technical.
[00:07:53] Candace Thille: Does that sound really sciencey? Um, now, in order to make a good decision, really we would need to consider features about that human learner. We'd also need to consider features about the thing they're trying to learn, and really importantly, features about the context in which that learning is occurring, or in which that knowledge will be applied. Now, if you think about it, trying to think about what do I know about the learner? What do I know about what they're trying to learn?
[00:08:23] What do I know about the context we're in right now? And putting all of those variables together and thinking about, given that, what's the best thing for me to help this learner? That is way beyond our human capacity for managing features, but it's not beyond our algorithms. So I'm not talking about using algorithms to replace teachers, but to support that process of human decision making, for both the learners and for the teachers, and for people designing, uh, learning assets. And for, you know, my favorite group, learning researchers.
[00:09:00] Russ Altman: Great. It's great. So thank you for that. And that allows me then to ask you about some of your historical work, which the, the comments you just made that help us understand why you would do this. So one thing that you did, that you did, I, I think even before you came to Stanford was called, I think the Open Learning Initiative. So now that, that'll put some meat on the bones. Tell me like, what was that? How did it work? Why did it exist?
[00:09:21] Candace Thille: Okay, thanks. The Open Learning Initiative started in 2002. So, uh, from education and AI terms a long time ago, and
[00:09:32] Russ Altman: Definitely pre AI era.
[00:09:33] Candace Thille: Pre, uh, well, pre generative AI. We were actually using AI even then, and I'll talk about that in a minute. So, uh, so what happened was the William & Flora Hewlett Foundation, um, had just funded OpenCourseWare, which was MIT's project to make all of their, uh, course materials openly available online, and they were going around asking other universities to make open coursewares. At Carnegie Mellon we were really happy about the open idea, but the challenge was to really support someone to learn something you need more than just course material. You need instruction. So building on the decades of research we had done in cognitive tutoring and other learning sciences, our pitch was to create a smaller number of courses, but that they were designed to support humans to learn, um, using what we knew about the science of learning.
[00:10:30] And then, then I started just collecting the data from the learner actions, 'cause I wanted to know, was there evidence that what we were designing was actually helping people to learn something. Uh, and then about a year after we started the Open learning Initiative, we got one of the big NSF, uh, science of learning grants, where then the OLI, uh, courseware became a research environment where we were collecting those data, not just to understand were the things we were designing helping people learn, but also to then do experimentation in different strategies to see what actually helped learners.
[00:11:08] Russ Altman: So can you take, can you paint me a picture of what a learner experienced as part of the, uh, Online Learning Initiative? Like what was their learning experience? I'm taking it that, I'm taking that they weren't just sitting in a classroom with a sage on the stage, uh, lecturing at them.
[00:11:22] Candace Thille: No, no. The, uh, one of the things we knew back then is that learning is, uh, as, as one of our articles said, learning is not a spectator sport. Um, that what you need to do to learn is you learn by the process of trying something and then getting feedback. And that feedback can be, you know, feedback from, oh, you got hit in the head. That's a kind of feedback, um, by the ball when you threw it wrong or something. But another kind of feedback is timely and targeted feedback that can understand the move you made.
[00:11:58] And make an inference about why you made that move and then give you explicit feedback about, oh, well, it looks like you did that because you were thinking this. Part of that was right, but this is where you might have gone off track. And then, and then most importantly, the opportunity to practice again once you've gotten the feedback. So the OLI courseworkware had small amounts of text. No, no video at that point in time, 'cause we're talking 2002, no lectures. But small amounts of text and then lots of interactive activities where the students could try something, get feedback, get hints if they needed, uh, if they weren't sure what to do.
[00:12:41] Russ Altman: Was it a mostly, so it was an online experience? It was a, it was a computational experience?
[00:12:46] Candace Thille: Completely. It was completely online.
[00:12:49] Russ Altman: Completely online. And did it also involve any other students, or was it kind of a one-on-one tutoring type setup?
[00:12:56] Candace Thille: This was a one-on-one tutoring type setup. And that was, that was in line with the goal of the Hewlett Foundation at the time. Which is they were trying to make, quote unquote, a high quality post-secondary education available to anyone in the world that had a good internet connection.
[00:13:14] Russ Altman: Great. So how did, uh, OLI or the Online Learning Initiative, how did that evolve over time? I know it did.
[00:13:20] Candace Thille: Oh yeah. It totally evolved over time. Uh, uh, and it, it, so I, I should say the way that we developed the courseware, um, in OLI was not just, I thought, oh, here's a great course, let's build it. Uh, a key ingredient of the OLI project was collaboration. Interdisciplinary collaboration. So we brought people that were experts in their field together with learning researchers, together with UX designers, uh, user experience.
[00:13:50] Russ Altman: User experience, yes.
[00:13:51] Candace Thille: Uh, together with, uh, software engineers and together collectively designing the environment. Um, the other thing that was really kind of cool about it is, uh, faculty from different disciplines, like we were building courses in biology and in chemistry and in statistics and in logic and in economics. Um, all these different, uh, faculty coming together, talking with each other about the challenges they were having and the solutions they were coming up with. So it started this incredible interdisciplinary dialogue across different fields of, what we think of different fields of higher education, but everybody focused on how do we support the learners to get these concepts.
[00:14:36] Russ Altman: Were the outcomes good?
[00:14:37] Candace Thille: The outcomes were great, actually, uh, uh, probably the most, uh, the, the study, we're constantly doing studies, but probably the most, uh, famous study we did was called The Accelerated Learning Study. And that was, and that was again, my program officer at the Hewlett Foundation, Mike Smith said, Candace, you're telling me all this stuff about using what we know about human learning, uh, to design environments. How, how, how do you know it's working? How do you know it's better? And I'm like, you know, I tried to explain how educational research is challenging. It's hard to get good effect sizes, et cetera. And he's like, okay, well show me they can learn faster. I said, faster? He said, yeah.
[00:15:21] So we did a study where we took introductory statistics course students and randomly assigned them to condition. In the traditional condition they took the traditional statistics course, which is 15 weeks in length and 4 class meetings a week. In the OLI condition they completed the same course but in 8 weeks with 2 class meetings a week. So, you know, so half, half the class time, a quarter of the instructor contact hours. And, uh, then we used measures of, uh, both, both the, they all took the same midterms and final. So we had those measures, but we also used another measure developed by a different research group measuring, uh, knowledge of introductory statistics as pretest and post-test.
[00:16:15] We also sampled both conditions during the study to make sure that the people in the OLI condition weren't just spending a lot more time in those 8 weeks on statistics per day or per week. And the results were that the students in the OLI condition on the midterms and finals performed as well or better then students in the traditional condition. And they performed 18 points better than the students in the traditional condition on the, uh, external measure of statistics knowledge. A funny thing about that I wanna share is that the faculty member, the, the faculty member that was doing the 2 meetings a week with the students, he, before the study, he's like, Candace, this isn't going, I'll do it, but it's not gonna work.
[00:17:03] And it's not gonna work because my students learn in relationship with me. And if I'm only seeing them for 8 weeks and for twice a week, I'll never get, that, that won't happen. And after the study, he said to me, this is the best experience I've had teaching statistics in my 15 years of teaching statistics. And I asked him why, and he said, well, for the first time coming into class, the students knew what they knew and what they didn't know. I knew what the students knew and what they didn't know. But probably most importantly, the students knew that I knew what they knew and didn't know. So we could spend that, you know, 50 minutes we had together focused on authentically, this is where you are, this is how I can support you.
[00:18:02] Russ Altman: Really great story. And so that is very important for everyone to know, 'cause it shows that, um, really this is incredibly important to get out into the world. Now I, I know you've done many things since, but one of the things that's fun to hear about is you, you did a brief stop or not so brief stop in, in the, in the tech industry at Amazon. How did that happen? How was that? Um, how does it inform your work today?
[00:18:25] Candace Thille: Okay, so, so I, I went from Carnegie Mellon to um, Stanford and I was at Stanford for about 4 or 5 years continuing this work. And I got a call to come up and do some consulting for Amazon, 'cause they had a particular workplace learning problem they wanted to solve. So I came up and consulted to them and I left and thought, okay, they're not gonna do what I said, so that's fine. And I said it to the VP who asked, who had invited me and she said, well, if you wanna do something different at Amazon, you write this thing called a PRFAQ. And that's a press release of when the intervention that you've designed is released into the world, what will be written about it.
[00:19:07] And then the FAQ is all of these different questions, the first being who's the customer? What's the customer problem? And then the rest of the questions are, how are, how do you think your solution is gonna act? What's your evidence? So I wrote one of those about my proposal, and they decided they wanted to do it. But they said, uh, but this isn't a consulting gig. You need to come up here to Seattle and, and lead this team. So, um, the, what my team, uh, did was, our goal was to innovate and scale workplace learning for Amazon. So the idea was, uh, I mean, Amazon at the time had 1.6 million employees and a need to rapidly upskill people in complex capabilities.
[00:19:58] And they recognized that the old mechanisms for doing that just weren't cutting it. And they also recognized that they were looking at their growth trajectory and didn't think that higher education was gonna be able to supply them with enough talent to meet that growth trajectory. So they wanted to develop an infrastructure and a system for rapidly developing complex capability. And that perfectly aligned with my research and also as you, as you can imagine, I'd spent the last 15 years trying to build the technical infrastructure using NSF funding and private foundation funding and graduate students.
[00:20:38] And here was a well-resourced company that said, we wanna, we want you to build what you wanna build. So come here and we will give you the resources to do it. My, uh, my hope was, and I said this to them, that my, you know, my real passion is for not-for-profit higher education. So my hope is once we build it and demonstrate it works, that you will give it away. Uh, they, they chose not to do that, but, you know, that was my hope.
[00:21:09] Russ Altman: But you have, you still have the knowledge and the takeaways from that experience.
[00:21:13] Candace Thille: Absolutely. So then, uh, that's why, so I, I, I took a leave of absence from Stanford. And I actually ended up having to resign from Stanford because only two year leave of absence. But then fortunately, my colleagues and Stanford wanted me back, so I did get rehired. And, uh, so now I'm building and extending what we built, uh, for the public good.
[00:21:37] Russ Altman: This is The Future of Everything with Russ Alman. We'll have more with Candace Thille next. Welcome back to The Future of Everything. I'm Russ Altman. I'm speaking with Candace Thille from Stanford University. In the last segment, we learned about learning science, how to think about learning, and what are some of the challenges in making sure that learning environments for learners are effective and work in the real world. In this segment, we're gonna talk a little bit more about this relationship between research and practice and how to optimize it. We're also gonna talk about how AI is creating great new opportunities for much richer environments for people to learn it.
[00:22:29] Candice, in our conversation, you made a reference to the relationship between research on learning and then turning it into practice, and I feel like there's still, there's still some conversation to have about that. So what is your current way to think about, I don't know if it's attention, but it's certainly two different activities. Learning how, learning how people learn, and then translate that, translating that into effective teaching and learning strategies. So where are we with all that?
[00:22:55] Candace Thille: Okay, so I'm gonna first push on that assumption that that's a linear process. That's what I'm trying to change. That the, the, our notion is we researchers do our research, we create our causal claims, and then we throw it over the, over to practitioners and say, implement this and people will learn better. And when practitioners then take our research and try and implement it, and it doesn't work. And then we say, oh, that's 'cause you didn't implement it with fidelity. You didn't, you didn't do it exactly the way I designed it. And then practitioners reasonably say, nah, you designed it with all these artificial constraints that don't exist in the real world. So that causal chain that you created, that, that exists for your system, it doesn't exist in the real world.
[00:23:48] So, so what we need is, rather than this linear model, to engage practitioners as collaborative researchers, and what I mean by that is, everybody who tries to teach someone something, you can think about that as you're testing a hypothesis. You think, okay, here's my learner. I think if I do this, it will help them learn what I'm hoping they'll learn. And if you think about that as generating an observation, and then we have an infrastructure that we can collect all of those observations, then our expertise about teaching and learning can be, not just from these scientifically controlled studies, not just from our own personal intuition or observation, but from being able to see the observations essentially that everybody's making. Which then allows us also to include a lot more diversity and a lot more voices in our science.
[00:24:49] Russ Altman: It sounds to me both totally reasonable, what you just said, and a lot more complicated to like orchestrate.
[00:24:57] Candace Thille: Yes.
[00:24:57] Russ Altman: Is that fair?
[00:24:58] Candace Thille: It is fair. Um, and that's where the, that's where a lot of the emerging technology can be really helpful. I think in, I think that vision I have of practitioners as collaborative researchers in place-based learning is very difficult to orchestrate. But if you have the right technical infrastructure, then every move I make can be captured and as an observation without my having to, to structure and experiment or do extra work, or what have you. So really it's the technical infrastructure that's the, the, the magic sauce.
[00:25:37] Russ Altman: Great. So it, it's remarkable. We've gone, you know, 20 minutes without saying the word AI or the letters AI. Tell me is, is AI, you, you kind of just implied that AI could be part of the solution. Uh, and so tell me, and particularly generative AI. So have you and your colleagues like assessed the value there and how, where, where is, what promise, if any, is it, is it, um, providing?
[00:26:02] Candace Thille: Okay. So I would say, uh, first I do want to make a distinction between AI and generative AI. Because, uh, I've been using AI to support human learning for over 20 years, and mostly in the old style of AI where we are collecting the learner's actions and then modeling those actions. And then from that, making an estimate of where the student is now. Also using AI to, given where the student is now and where the student's trying to get to, uh, make a recommend, build recommendation models based on past evidence about what kind of learning experience is gonna have the highest probability of supporting the learner.
[00:26:45] Russ Altman: It's almost like Netflix or Amazon, but for a learner.
[00:26:48] Candace Thille: For learners, right.
[00:26:48] Russ Altman: You need to watch this movie. You need to do this module of learning.
[00:26:51] Candace Thille: Yes. So, so it's, uh, I used to always talk about, uh, the, I think that's the power of the technology is not that we, uh, build learning experiences, uh, but that we build them at an interface. Because in that interface, in that computer interface, we can observe the learner. And we can observe the learner, just as you said, like Netflix does, like Amazon does. Uh, they're trying to do it to understand you better as a consumer.
[00:27:20] Russ Altman: Yes, very different motives.
[00:27:21] Candace Thille: Very different motives, but we're trying to do it to understand you better, you individually as a human learner better, but also to understand learners as a collective, the processes of human learning better, and that's the power of this technology. The ability to collect information from a well-designed interface, model that information, and use that to give feedback to the human actors in the learning system that would be learners themselves, giving them feedback. Giving feedback to instructors, as we've talked about, giving feedback to designers that say, you know, that asset that you thought you designed to help someone learn this, it's not doing so well. And here are some recommendations about how to improve it. And then feedback to learning researchers so we can start to extract, uh, fundamental principles of human learning. So that's the power of, sort of, using the, I think, old AI technology.
[00:28:22] Russ Altman: Yeah. But maybe we'll call it predictive AI.
[00:28:24] Candace Thille: Yeah. There we go. Uh, the, uh, the power of the generative AI, of course, is in the communication interface. I mean, for decades I've built dashboards to try and communicate to learners, this is where you are, this is where you are trying to get to, et cetera. These are some actions you could take next, or to instructors with similar kinds of information. And you know, and dashboards can be great, but often people don't know how to interpret them. They feel overwhelmed by the information. They're not super effective actually, uh, in this context. Uh, so, uh, the, here's a power of generative AI, because it's a communication, it, it interprets language.
[00:29:12] So I could give the AI all the insight that's in the dashboard and rather than having you try and look at a dashboard, you could, you could look at a dashboard, but you could say, well, tell me this. And the AI can use the data that it has access to to give you the information you need and engage in a conversation with you around it. So I think a big part of the generative AI is in that human, um, interaction, uh, interface. The other part is, the other thing that generative AI is doing is making, uh, technical skills much more accessible to other people. For example, if you are trying to draw something, a draw process, like I wanna, I wanna draw a diagram for you that explains osmosis or something.
[00:30:05] If I use paper and pencil, then I'm very focused on how's my circle look, or whatever. But now if I can describe it to an AI and it can draw a good graphic representation, and even in the process of my describing it, I'm having to refine my understanding of it, then that active creation does two things. It supports a different way of supporting someone to learn something, learn through creation, where the focus is on the actual thing that you're trying to design and not on, how well did I draw that circle? But it also, for instructors or for teachers, allows me to build a very, uh, effective and professional learning experience to get out things into the world that I want people to understand in the way I want people to understand it. So I think it gives, it's an opportunity to redistribute voice and power. Uh, that's my other excitement about it in learning.
[00:31:01] Russ Altman: So this, yeah, this really does, it sounds very exciting and it sounds to me like you're taking all of the learnings from the pre generative AI era, the predictive parts, and you're marrying them to the generative part as, as part of a richer interface and a, and a more adaptable interface to the learner. I'm guessing, uh, and and you, you actually said it already with this, um, example of drawing. I'm guessing that this doesn't just have to be a textual interface. It could involve, um, uh, 'cause people are always saying, sometimes, I've heard of many times people say, I'm not a book learner, I'm a visual learner. And it sounds like there's a space for all of those styles because of the generative AI. Am I extrapolating too much in saying that?
[00:31:40] Candace Thille: No, no. You're, you're, you're right. But I also wanna correct something.
[00:31:44] Russ Altman: That's perfect.
[00:31:45] Candace Thille: Just when you said, you know, I'm a visual learner, people have these ideas. I'm a visual learner. I'm an auditory learner. I'm a kinesthetic learner.
[00:31:53] Russ Altman: Yes.
[00:31:53] Candace Thille: Yes. And I just wanna say that's a great example of where my field let us all down. Uh, we had a theory of, uh, that there were differences in how people process information, and that if you could only know your learning style, or a teacher could only know your learning style and give you that information, in that style, you would learn better. That theory kind of got out into the world before there was actually empirical evidence.
[00:32:21] Russ Altman: Oh my goodness. Oh my goodness.
[00:32:23] Candace Thille: And a whole industry developed around learning science.
[00:32:25] Russ Altman: I did not mean to perpetuate a falsehood, so I apologize.
[00:32:29] Candace Thille: You're not the only one. I mean, it's always, I, I, I love the opportunity to, to debunk that myth.
[00:32:37] Russ Altman: It, it makes, it makes really good sense because I know that I, I never really pigeonholed myself into any learning type. And in fact, my instinct was always depending on what I'm learning, I'm all of the above.
[00:32:48] Candace Thille: There you go. And all, and what we do know, the good thing about it is we do know that multiple representations support learning. So that if I can give you some, if I can, if you can experience something kinesthetically, if you can experience it auditorily, if you can experience visually, then all those multiple representations will actually support your learning. But, but people, I mean, there are, I mean, people have physical differences, so there are some people that, that one, uh, mode is better. But for the general population, you are right that your learning style is inextricably intertwined with, uh, what you're trying to learn.
[00:33:30] Russ Altman: Well, this is great and thank you for painting a picture of, of the future of learning that is extremely exciting. Uh, before we end, uh, we have our segment called the Future In a Minute, and I'm wondering if you are ready for me to ask you some short questions and for you to give me some, uh, short answers.
[00:33:46] Candace Thille: Yes, I am ready.
[00:33:47] Russ Altman: Okay, great. Thank you very much. So, uh, I will read them and the first question is, what is one thing that gives you the most hope for the future?
[00:33:55] Candace Thille: Okay. Uh, the new tools, if we design them well, can give voice to people to participate who previously did not have voice.
[00:34:06] Russ Altman: What's one thing you want people to walk away from this episode remembering?
[00:34:11] Candace Thille: Uh, the science of human learning is at the start of a scientific revolution in understanding human learning. Everyone can and should participate.
[00:34:24] Russ Altman: Aside from money, what is the one thing you need to succeed in your research?
[00:34:29] Candace Thille: A good data infrastructure that is for the public good.
[00:34:35] Russ Altman: If all goes well, what does the future look like?
[00:34:38] Candace Thille: The science of learning is continuously improving itself so that we have the ability to support all learners to have agency and learn what they want to learn globally.
[00:34:54] Russ Altman: And finally, if you were starting over again and you needed to get your certification or your degree in a different discipline, what would it be?
[00:35:01] Candace Thille: You know, that's hard 'cause I love, I mean, I have degrees in, in education and computer science and then also in social science and literature. So, so I kind of did that. Uh, but if I were to study, if I were gonna study something new, it would be something completely different. Probably music, 'cause I know nothing about music.
[00:35:24] Russ Altman: Thanks to Candace Thille. That was the future of learning. Thank you for listening to The Future of Everything. We have a lot of episodes in our back catalog, and so please check it and make sure that you get all the content you might be interested in so you can learn about The Future of Everything. Also, you know what? If somebody popped into your head while you were listening to the interview with Candace, why don't you recommend the show to them? They might enjoy it as well, and if it popped into your head, maybe you learned that they should be a listener as well. You can connect with me on many social media platforms including LinkedIn, Bluesky, Threads, and Mastodon, where I'm @RBAltman or @RussBAltman. You can also follow the Stanford School of Engineering @StanfordSchoolOfEngineering, or more short @StanfordENG.
