In a talk about critical thinking, Daniel Willingham uses the example of his friend who asks his daughter – let’s call her Zarah – to buy him an Americano from Starbucks. When Zarah comes back with more than the expected amount of change, she tells her father that she knew that an Americano was hot water and espresso, noticed that buying an espresso and hot water separately was cheaper than buying an Americano, and did so.
This anecdote aptly illustrates the kind of critical thinking we hope to see from our students because Zarah transferred her mathematical skills and knowledge about coffee to a new situation. Yet, many tests of transfer tend to make people “look dumb” according to Efficiency in Innovation and Transfer by Daniel Schwartz, John Bransford, and David Sears. Too many tests of our ability to learn and transfer knowledge involve what the authors call Sequestered Problem Solving (SPS), which is effectively what Zarah did in the coffee shop. She wasn’t given the chance to ask questions, look-up information, or receive any feedback on her performance which are exactly the skills that we want students to walk away with. Yet, unlike Zarah in the coffee shop, we don’t always have everything on hand – or in our heads – that we need to effectively solve problems, and that’s why SPS tests of transfer make us “look dumb.”
A classic SPS challenge studied by Kay Burgess – designing a recovery program for eagles – can make fifth graders and college students “look dumb” and make schools look ineffective at preparing people to solve real-world problems. However, if we modify the challenge and ask the participants what questions they would like answered or ask them to come up with an action plan they would follow to learn more about the challenge, then the results look significantly different. Schwartz, Bransford, and Sears write: “The questions generated by the college students were much more sophisticated and should ultimately provide better guides for future learning than the questions asked by the fifth-graders.”
Schwartz, Bransford, and Sears advocate for a different paradigm of transfer that they call Preparation for Future Learning. Instead of looking to see what problems people can solve when they are sequestered and isolated, the authors are interested in what skills and abilities people have to learn how to solve new problems. Experts bring rich schemas to their interpretations and notice things that novices don’t.
For many new situations, people do not have sufficient memories, schemas, or procedures to solve a problem, but they do have interpretations that shape how they begin to make sense of the situation. We know from a number of literatures—including the perceptual learning literature, the expertise literature, the problem-solving literature, and the cognitive therapy literature—that what one notices about new situations and how one frames problems has major effects on subsequent thinking and cognitive processing.
What role should education play? They argue that we need to balance out two dimensions of learning: efficiency and innovation. On the one hand, Zarah used her interpretation of the situation, based on her background knowledge, to come up with innovative and new possibilities. Without the right background knowledge, she wouldn’t even have been able to see there was a problem that needed solving. On the other hand, Zarah needed to be able to efficiently carry out the mental math that was necessary to choose the espresso and hot water combination. Perhaps that mental math was even so fast and subconscious that it felt effortless.
In schools, we need to actively encourage kids to look for new problems and give them the room and permission to experiment. We need to reward taking risks instead of punishing kids with grades. I see the most mistakes in sentence structure happen when students consciously break away from what they know to be safe, and take risks based on the good writing that we read. Yet, innovation doesn’t come from nowhere, and schools must also encourage building efficient and fluent expertise. Perhaps innovation and efficiency are not orthogonal dimensions, since as Willingham argues in his talk, the best route to seeing the “deep structure” of problems is practice.
Schwartz, Bransford, and Sears articulate a balanced position:
Our argument is not to eliminate efficiency but to complement it so that people can adapt optimally. In short, we assume that efficiency does not have to be the enemy of innovation and creativity (e.g., Bransford & Stein, 1993). For example, it is well known that efficiency in some processes (e.g., learning to drive a car, learning to decode written words and sentences) frees attentional capacity to do other things (e.g., talking while driving, reading for meaning, Atkinson & Schiffrin, 1968; LaBerge & Samuels, 1974). Similarly, if people confronted with a new, complex problem, have solved aspects of it before, this helps make these subproblems routine and easy to solve. This frees attentional bandwidth and enables people to con- centrate on other aspects of the new situation that may require nonroutine adaptation. A major theoretical challenge is to understand how efficiency and adaptability can coexist most effectively.
Following Bloom’s taxonomy and Understanding by Design, many people in education see knowledge as consisting of the memorization of a discrete list of facts. That’s hard to get excited about. But if we see the brain as being in the business of actively predicting what will happen and using various pattern completion networks, then our background knowledge assumes an inherently active role in making us sensitive to things we might have missed or possibilities we didn’t know existed. For example, Andy Clark’s “hierarchical predictive processing account” of the brain makes “the lines between perception and cognition fuzzy, perhaps even vanishing.” Our brains do not passively receive information from the outside, but actively predict and compare those predictions to input. Clark sees the “prediction-error minimization as the driving force behind learning, action-selection, recognition, and inference.” Not only do our brains actively predict, but we humans actively change the world to see what will happen. “Change, motion, exploration, and search are themselves valuable for creatures living in worlds where resources are unevenly spread and new threats and opportunities continuously arise.” Our brains are not sequestered spectators in a static world.
But John Dewey knew that already. In Reconstruction in Philosophy (1920), Dewey argues against the longstanding Platonic bias of epistemology: “We tend to think of it [knowledge] after the model of a spectator viewing a finished picture rather than after that of the artist producing the painting.” We’re still waiting for education to drop the bad epistemology which it inherited from philosophy and let knowledge become experiential.
artists in tate modern by hyun seok jeong