Our nation is rather good at grooming youngsters to become excellent athletes. So a Chinese physicist who is doing his post-doctoral research at the Massachusetts Institute of Technology wondered if college physics could be taught the same way that coaches teach soccer, and get better results. Just the way that great soccer coaches break the game down into specific skills, such as passing, receiving and shooting, and have kids practice these skills until they master them, Zhongzhou Chen is trying to break down the field of physics into simpler skills that students can master through practice.
“We really want students to be masters at problem solving,” said Chen, when I talked to him by phone. “They’re smart kids, but they’re constantly slowed down by basic things that we as physicists take for granted. They spend so much time struggling with these basic things.”
Chen believes if students become more facile with the basics, their brains will be less cluttered and freer to come up with creative insights, just as a soccer players can focus more on reacting to the play on the field if their kicks and footwork are second-nature. He says he was inspired by the educational theories of Swedish psychologist Anders Ericsson, who documented how experts, such as professional classical musicians or chess players, become very good at their skills through training, or “deliberate practice,” instead of through innate talent.
Of course, some students will eventually master many of these skills if they continue on the road to obtaining a Ph.D. But deliberative practice might help them get to the fun, creative part of physics sooner. And for weaker students who are at risk of failing, Chen hopes that deliberative practice can lead to success and keep them from dropping out.
In college physics classes, students typically practice skills through problem sets. But a traditional problem set might require the application of a dozen skills in combination. Over the course of an hour or two, a student might practice any particular physics skill only two or three times — not enough to become fluent in and dexterous with it. Chen thought it might help students if they could apply a physics technique, such as the tricky “right-hand rule” in electromagnetism, many times, in different ways, at one sitting.
Chen says he’s not advocating “drill and kill”-style rote memorization, or the plugging of numbers into formulas. But he does want to drill concepts, such as making students translate real-world situations into mathematical expressions and vice versa. “The tasks I like are ones, if you have rote or superficial understanding, you will fail,” he explains. “I like to create tasks that require conceptual understanding.”
Of course, it would be too expensive to hire a personal physics “coach” for each student. And a teacher of 100 students cannot give the kind of instant feedback that Chen says is necessary for deliberative practice to work. But a computer could.
He was particularly interested in creating computerized “drag-and-drop” exercises, where a student can move an object to an infinite number of possible places — simulating real world situations — instead of multiple-choice exercises, which can be mind-numbing and artificial. (An example of a drag-and-drop exercise is depicted in the graphic at the top of this story.)
As a scientist, Chen wanted to test whether this online “drag-and-drop” practice actually helped students perform better in physics. So he devised an experiment, along with three other physicists, including MIT professor David Pritchard, within one of MIT’s physics MOOCs, free online courses for the general public. The researchers rotated all the students through three types of exercises — traditional problem sets, multiple-choice exercises and drag-and-drop practice — to see when they did the best. Chen presented his results in a working paper, “Evidence of Short Term Learning from ‘Drag and Drop’ Deliberate Practice Activities,” on Oct. 2, 2015 at the Learning With MOOCS conference held at Teachers College, Columbia University.
He was refreshingly honest about not landing upon instant success. The students who used drag-and-drop practice didn’t do any better on the course’s quizzes than the students who practiced with traditional problem sets. (The students who practiced skills through multiple-choice exercises did the worst, which is discouraging, since so much online instruction employs multiple-choice questions.)
Chen isn’t discouraged — yet. He believes his experiment’s design was flawed. Too many students in MOOCs already have graduate degrees and aren’t in the undergraduate population he’s trying to target. And he suspects that many students were completing their online assessments long after the deliberate practice sessions, with their physics books open and Googling answers.
He also thinks it’s possible that he didn’t choose the right concepts for students to drill. The holy grail, of course, would be to give each student a personalized set of drag-and-drop problems that targeted individual weaknesses and knowledge gaps.
In the future, Chen hopes to design a better experiment within an online physics course, and he’s hoping to collaborate with a professor of a face-to-face physics course on creating online materials that traditional undergraduate students could use to hone their skills.
It’s possible that computerized deliberate practice will never work in science education. But what I love about this research is that Chen is a theoretical physicist who also has considerable experience teaching physics in college classrooms. If we’re going to find ways to use technology to have breakthroughs in learning, it’s probably going to come from educators like Chen, in academic laboratories where there aren’t financial pressures to bring half-baked ideas to market too soon.
This article also appeared here.
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