Learnlets
Clark Quinn's Learnings about Learning
(The Official Quinnovation blog)

15 October 2009

Game-based meta-cognitive coaching

Clark @ 8:01 am

Many years ago, I read of some work being done by Valerie Shute and Jeffrey Bonar that I later got a chance to actually play a (very small) role in (and even later got to work with Valerie, definitely world-class talent).  They had developed three separate tutoring environments (geometric optics, economics, electrical circuits), yet the tutoring engine was essentially the same across all three, not domain specific.  The clever thing they were doing was tutoring on exploration skills, varying one variable at a time, making reasonable increments in values to graph trends, etc.

Subsequent to that, I got involved again in games for learning. What naturally occurred to me was that you could put the same sort of meta-cognitive skill tutoring in a game environment, as you have to digitally create all the elements you’d need to track anyways for the game reasons, and it could be a layer on top.  While this would work in a single game (and we did put a small version into the Quest game), it would be even better on top of a game engine.  I even proposed it as a research project, but the grant reviewers thought that while  a good idea, it was too ambitious (ahead of my time and underestimated :).

The reinterest in so-called 21st century skills, the kind Stephen Downes so eloquently calls an Operating System for the Mind, reawakens the opportunity.  These skills are manifested in activity, and require an understanding of the activity to be able to infer approaches and provide feedback. In a well-defined arena like a designed game environment, we can know the goals and possible actions, and start looking for patterns of behavior.

Game engines, with their fixed primitives, make it easier to define what goals are and consequently to specify the particular goals and makes looking for patterns more generally definable.  Thus, in a game, we can see whether the learners’ exploration is systematic, whether their attempts are as informative as possible, and possibly more.

This is also true of virtual worlds, although only when designed with goals (e.g. from a simulation to a scenario, whether tuned into a game or not).  The benefit of a virtual world is, again, the primitives are fixed, simplifying the task of defining goals and actions.

Of course, building particular types of interaction (e.g. social), particular types of clues (e.g. audio versus visual) and looking for patterns can provide deeper opportunities.  Really, such performance is initially an assessment (one of the facets of what we were doing on the Intellectricity project was building a learner characteristic assessment as a game), and that assessment can trigger intervention as a consequence.  For any malleable skill, we have real opportunities.

Given that much of what is necessary are abilities to research , evaluate the quality of sources, design, experiment, create, and more, these environments are a fascinating opportunity.  I’m not in a situation to lead such an initiative, but I still think it’s a worthwhile undertaking.  Anyone ‘game’?

1 Comment »

  1. Game? Yes. In a position to lead such an initiative? Alas, no…

    Comment by Rob Moser — 15 October 2009 @ 6:16 pm

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