字幕表 動画を再生する 英語字幕をプリント JOSEPH JAY WILLIAMS: So I just want to mention, my background is in cognitive science. So I'm really interested in doing experiments in terms of understanding how people learn. And to get a sense of the way that fits in with the research landscape, because there's a ton of research on learning and many insights. I guess the best concept is thinking in terms of qualitative analyses. So these rich investigations that you see from a sociological perspective. Education schools that will take a student and really take a very close look at what they're learning. Explore how they're understanding of algebra and all the difference misconceptions they can have. Then there's also things that are much more like randomized control trials. So policy workwear. You might take a bunch of students in the school and give them a treatment where they undergo a training program. And then you can see if that actually has an effect compared to control group. And that's a very large scale. There's longitudinal studies, which, again, are very long time scale. Collecting measures, like people's grades or students' outcomes as they progress through traditional education. And of course there's working computer science and Educational Data Mining, where you take very large data sets in terms of observations, then try and induce what's going on with learners. As is terms of the way cognitive science fits in with this, I think it's in between something like a randomized control trial, longitudinal study and some light qualitative analysis. Because most of the experiments we do on a short time scale. And it really involves precisely controlling what someone's learning in a particular situation and trying out different forms of instruction and then assessing how learning is occurring after that. And you might think that from such micro experiments you can't learn much. But that's actually the expertise in cognitive science. And I think it's ready insightful. It's obviously using a lot of insights. But I think it's ready well suited for online education, where often you want to ask questions that intermediate between again, quality of assessment of what's going on, and running a full-out randomized control trial where you give people two different versions of the course. There are questions about how you should frame instruction, what kind of video you should show someone, and the kind of strategies you should teach them. So it just lets you know where it sits in there. And what's also nice about cognitive science as an approach is it's pretty interdisciplinary. In a sense, you've got psychology, which is heavily focused on experiments. Philosophy, in terms of really trying to do good conception analysis of what problem you're facing and classifying different kinds of learning. Linguistics, anthropology, as I mentioned, neuroscience, and of course, AI. And what's also nice, I think, is that it's also a bit easier for people in cognitive science perhaps to talk with the kind of researchers at Google who are interested often in things like modeling and machine learning. So to give you a bit of a preview of [INAUDIBLE] cover. So I'm going to talk about two ways you could think about learning. One that I think gives us a lot of rich insights into how to improve education. And I'm going to talk about very quickly three finds in cognitive science that I find have been particularly powerful. It's thinking about what can you do before someone starts learning in terms of framing their learning as an answer to a problem? What can you do during learning in terms of requesting explanations from the learner? And then what can you do after learning? A new and interesting finding here is that you can actually use assessments as instructional tools. Having people take a test can actually be more powerful for learning than having them study material again. And then having looked at some of what we know about how to promote learning of a concept or some set of knowledge, it's worth asking, well, what knowledge can we teach someone that's actually going to have the biggest impact practically? And that's why I think thinking about this idea of what do people learn that's going to help them learn more? So it's not just on this concept but across a range of situations. And then I'll talk about how you can change people's beliefs and have a massive effect on their motivation. You can teach people strategies for learning that then have trickle down effects on all the content they may come across. And then finally, I'm going to talk about online search, which is something I really started to think about seriously recently since looking [INAUDIBLE] of a power searching course. And I actually think this is a really fascinating topic that's right at the junction of things that are really important to industry and the business world, and also really important to education. And I think that's actually a great place to focus, because it allows you to get the benefits of private innovation as well as academic work. And also it means that you can use insights across areas. When you're teaching a business person about search, you're also learning something about how you could help a child to be more inventive in the way they discover information on the internet. How is my speed in terms of talking? Because with an accent, it probably sounds like twice as fast. And I speak twice as fast, so you're hitting a lot of powers being raised there. OK. And one thing I'll cover at the end is I put on the website just a list of resources that I found really useful in terms of knowing what the literature is that has shown really impressive effects on learning. When you go in Google Scholar, I mean literally there are thousands of papers there. And I think that can be daunting. And it's just easy to start with your problem and work on that. But there's always tons of really interesting relevant work. And so I tried to put aside some of the resources that I found most useful. So I have hyperlinks to some of the papers there and brief explanations of what they're about. And also information about other things that I'll talk about at the end. So in terms of learning, I think one kind of challenge, even after studying learning for all my adult life, I still think that there's this kind of intuition or this intuitive theory that I have about learning that holds me back, or misleads me when I'm making instructional decisions. I think it's something that's sort of common across many of us. In the Cambridge Handbook of Learning and Sciences they refer to this idea that when you learn something, you're just dropping it into a bucket. Learning is transferred from the teacher to the student. So let's think about some content. For example, learning from a video, like from a power searching online video, a Khan Academy video. Or reading text, which is how we absorb most of our information. Or you could think about learning from an exercise. So a student solving a math problem, or someone attempting to process a financial statement or do some budgeting or solving a problem of managing relationships with coworkers. Anytime you can see those icons I need to represent, you can stick your personal content in there. So if you have a favorite example of learning or one that's particularly relevant to your everyday experience or your job, just feel free to insert that every time that flashes out. But I'll try to illustrate it with examples. Because again there's good evidence that that's an important thing to do for learning. So I think there's this intuition that what learning is about is just adding information. So it's almost as if we have this model where the mind is a bucket.