Last month we discussed the topic of adaptive expertise and with it, the concept of deep learning. Deep learning, a concept that is best depicted in the image above, has its roots in relationships. These relationships enhance the ability to engage students in their learning, allowing the “deep” to take place.
Recently, I stumbled upon an article written by last year’s Teacher of the Year. In it, he espouses the value of deep learning through the concept of teaching students, not subjects. Click here to read more (Teaching Students – Not Subjects).
If we take deep learning as a goal of teaching and learning, adaptive expertise is the conceptual map of how we might get there. Dr. Ken Bain, a noted expert on deep learning, asked a fantastic question when he posed, “What kinds of experiences are likely to produce the adaptive experts?”
His response was, “In the United States, Dr. John Bransford and others have found that lots of opportunity to speculate will foster that kind of adaptive ability. When we give students a messy problem and allow them to speculate, to work collaboratively to struggle with the evidence and craft tentative solutions, we foster their adaptive expertise.”
I love this. School should be about messy problems and big, seemingly impossible questions. That’s when learning occurs. Is it fair to say then that failing promotes learning? Dealing with messy unknowns is good, despite the frustration.
Personally, I prefer the word “fail” over struggle because it’s heavier sounding, though conceptually we are talking about the same thing. Our children at Allen Academy must struggle and fail (though I don’t necessarily mean an “F” on a test). How do humans learn best? Trial and trial and trial again. Be it the Wright Brothers, the early NASA astronauts, a sports team, or even parenting itself – we learn best by messing something up and trying to do it better the next time.
With adaptive expertise, humans can not only have the attitudes and skills necessary to achieve deep learning, but also thrive in a world that promises to be increasingly more unpredictable.