This past week I was in Taiwan as an invited keynote at the IEEE’s Digital Game and Intelligent Toy Enhanced Learning workshop. I like to keep my head in on the academic side as a source of inspiration, and this was just such an opportunity. I got there late (family commitment meant I missed the first two days) but I heard the last day with some great talks, and had a chance to read a lot of the proceedings on the long flight back.
One of the interesting outcomes was the debate about what’s a game and what’s a toy. Games have rules, toys have affordances (read: capabilities), but when your toys can communicate to you and each other, they start blending the boundaries. Another form of blending was that some of the game work was about classroom work, but the toy stuff tended to be more focused on non-school play.
Of course, there was some talk about supporting the learning, and the need for reflection. In addition to my expected coverage of systematic learning game design, one of the points I tried to throw in is that we should be looking at ways in which learning systems could be smart about coaching learning to learn and generalization, not just on the particular domain such as mathematics. There’s very simple coaching in the Quest game that focuses on your exploration, based upon some work Valerie Shute & Jeffrey Bonar did even longer ago, and I think that model of coaching could be expanded and built into any modeled environment (e.g. game engines).
I didn’t hear his keynote, but Michael Eisenberg, who I’d met years ago and has subsequently become a steadfast innovator at the University of Colorado Boulder (a great cog sci place), had another talk about making magic manifest, not having black boxes but making the operations manipulable so you can change them and explore the underlying relationships. Eric Schweikardt, a student of Michael’s collaborator Dan Gross, attempted a categorization across games, and pointed out a different model of programming that involves lots of distributed capabilities being pulled together into a smart aggregation instead of a central intelligent program (e.g. Lego Mindstorms), and presented several versions.
My notion of a wise curriculum includes thinking systemically and modeling skills, so the notion of using toys to learn different modeling schemes is very cool. Not to the exclusion of the central control model, but as an alternate approach (indeed, as was pointed out by Schweikardt, Stephen Wolfram has argued that we should be using small rules as the way to understand how the world works).
Another innovator with toys was fellow keynoter Masanori Sugimoto who is doing some very innovative things with manipulables, including a computer projector. (I made a note to add ‘projector’ to my list of potential input/outputs for mobile devices!) He also does very systematic studies of his implementations and tunes them to get them better. For instance, he was using a camera to register what elements kids put down where on a grid table, but the kids leaning over obscured it, so he had to make the pieces carry the information and have the grid itself record what was on it.
As Professor Tak-Wai Chan (our host, and a recognized innovator in his own right for his exploration of intelligent learning ‘companions’) noted, one of the reasons to have this overseas is to help make the US aware of how much happens overseas; one of the first lessons I learned when I went to Australia for an academic position was how insular the US is, beacuse there’s so much happening in the US it’s easy to miss how much is happening elsewhere.
Sure, there were some fairly straightforward exercises about games and toys, and some rather typical research, but we need these too. The next one will be a full conference in Europe, and I believe there’s a commitment to regularly move it around. The neat thing about this conference was that it not only about classroom learning but also about informal learning (and technology, ok so I’m still a geek), so it provided an interesting way to look at the intersection, and I think there will be great reasons to keep track of this direction. There were a lot of students, and there’s great hope that this research (as eloquently put many years ago by John Anderson that we learn alot about learning by trying to create learning systems) can make new inroads into understanding.