Jon,
I wish I had been able to see this presentation "live," but am happy to be able to see the slides. It is always amazing to me that these fundamentals about motivation seem either absent from conversations about learning or are overpowered by unexamined assumptions.
Gerald
Thanks Gerald! Yes - I suspect it is so deeply embedded that we don't see it for what it is, or just assume it is a given over which we have no control. But we do.
I think there are a couple of things we can say with certainty: teaching is one of the best ways to learn, and the learning that results from making or doing something is more durable and transferable than the learning about something that we cover in exams. I try to build my art on those two principles. Exams, and much of education, are, as you noted, mostly about enforcing compliance and not so much about learning. That's too bad.
Thanks Mary - yes to all that!
Although, in all cases, there are exceptions. Tain't what you do, it's the way that you do it (except maybe enforcing compliance, but even then there are a very few that occasionally benefit - diversity trumps generalization). And, as the song goes on, it's also the time that you do it, and the place that you do it, that's what gets results. And the reasons that these things are true are complex, situated and connected, which is precisely the problem. As long as we go from general to the particular, and lack any means of testing our theoretical assumptions or models by reductive methods, it implies we don't have a science of education. In fact, we almost certainly cannot have a science of education within existing reductive paradigms. We are learning how to better deal with complex adaptive systems and this may provide a meaningful way forwards - it is a genuinely different way of doing science with genuinely different methods and useful criteria for success. But even and perhaps particularly then, as you imply and as theory seems to suggest, the art matters - we can adopt some good rules of thumb but, in all cases, it is the creative, human, meaning-filled interpretation and extrapolation that matters, not the rules. I don't think that's such a bad thing. We are, as Stuart Kaufman put it, reinventing the sacred.
Jon,
Thanks so much for sharing this book and your commentary. I have just finished this book and am now reading Miller and Page's earlier Complex Adaptive Systems: An Introduction to Computational Models of Social Life. I am trying to immerse myself in the study of complexity in ways that would support my research into self-directed learning environments.
Like education is closer to an art and design rather than pure science. And I share your ambivalence about studies that focus on demonstrating an effect from changing just one thing (at least intentionally) and then sharing those results as though they were widely generalizable.
I had not been explosed to Christensen's method, which looks fruitful, if challenging.
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Right on! Why let facts get involved the discussion.
Of course, with the blatant media bias in most news stories, plus the willingness to simply repeat or requote from other stories, one could argue there really aren't any facts in most news stories, and reading the headlines is about as good as it gets. :-D
Too right, Jon. In many ways, past experience is often the *WORST* predictor of future performance, because it often ignores motivation.
Example: in Grade 9 my "teacher" lost my math mid-term (how I'll never understand). So he gave me "52% because you're probably mediocre". I was so angry that I determined to show the jerk and got 82% on the year-end departmentals. I also went on to get high 90's in my high school math courses.
Never underestimate the "I'll show you" effect. :-)
To quote Shawn Achor from his TED talk1, "If I asked a question like, "How fast can a child learn how to read in a classroom?" scientists change the answer to "How fast does the average child learn how to read in that classroom?" and we tailor the class towards the average." This ignores those above and below the curve.
I suspect that the issues surrounding analytics and their impact on our lives will become more pronounced as AI use increases. There will be many cases where an algorithm comes to the wrong conclusion. How can a clear feedback loop be identified to verify and correct misdecision?
Richard, I like the "I'll show you" effect. I'm going to use that one. I've had similar personal experiences as well.
Cheers,
Kyle
1 https://www.ted.com/talks/shawn_achor_the_happy_secret_to_better_work?language=en
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