Are students who learn faster the ones with more potential?

Every day, students of all ages are confronted with new concepts and skills, and some learn them more quickly than others. It’s easy to attribute this to natural talent—but if teachers do so, they might be opening doors for some students and shutting them for others.

In other words, there is a dark side to believing in innate talent. It can beget a tendency to assume that some people have a talent for something and others don’t, and that you can tell the difference early on. If you believe that, you encourage and support the “talented” ones and discourage the rest, creating a self-fulfilling prophecy.

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The best way to avoid this is to recognize the potential in all of us—and work to find ways to develop it, as some researchers are starting to do.

From chessboards to chalkboards

This essay is adapted from Peak: Secrets from the New Science of Expertise (Houghton Mifflin Harcourt, 2016, 336 pages).

In the game of chess, children with higher IQs generally find it easier to learn and remember the rules of the game and to develop and carry out strategies, giving them an early advantage in winning at chess.

But according to recent research, the most significant predictor of chess skill over time is not IQ—but how much children practice.

A similar thing may be true for math performance. Recent research has shown that children who have had experience playing linear board games with counting steps before they start school will do better in math once they are in school. And there are likely many other ways that preschool experiences that give children practice with math will help them perform better later on.

Most teachers, however, are not familiar with this research. Often, the kids that “get” math more quickly than others are generally assumed to be gifted at math while the others aren’t. Then the “gifted” ones get more encouragement, more training, and so on, and, sure enough, after a year or so they’re much better at math than the others. This advantage can propagate through the school years, creating larger and larger disparities among children.

Since there are a number of careers, such as engineering or physics, that require math courses in college, the students who have been judged to have no talent for math find these careers closed to them. But if math works the same way as chess, then we have lost a whole collection of children who might eventually have become quite accomplished in these areas if only they hadn’t been labeled as “no good at math” in the very beginning.

A case study: Revolutionizing freshman physics

We can combat this trend by looking at student potential in a different way. Educators can implement new teaching methods that give students a better chance to learn, methods that take advantage of what we know about peak performance and the importance of practice in developing skills and knowledge.

In a study conducted at the University of British Columbia, some students enrolled in a traditional freshman physics course were given a small taste of what this might look like. For the first 11 weeks, each cohort of the class received relatively standard instruction: three fifty-minute lectures a week, weekly homework assignments, and tutorial sessions where the students would solve problems under the eye of a teaching assistant. But, during week 12, one cohort was exposed to techniques developed by Nobel Prize in Physics winner Carl Wieman and his colleagues and taught by two researchers rather than the usual instructor.

Those techniques were based on the concept of deliberate practice, which research suggests is a highly effective and powerful tool for improvement. In particular, it is informed and guided by experts’ accomplishments and by an understanding of what these experts do to excel. According to research that we and others have done, this type of practice is key to attaining mastery in established fields ranging from music to sports to chess.

In the deliberate-practice cohort, the researchers assigned the students to read several pages from their physics text before each class and then complete a short online true/false test about the reading. The idea was to make them familiar with the concepts that would be worked on in class before they arrived.

When they came to the class, the researchers divided the students into small groups and then posed a “clicker question”—a question that the students answered electronically, with the answers sent automatically to the instructor. The questions were chosen to get the students in the class thinking about concepts that most first-year physics students find difficult.

The students were able to discuss each question within their small groups before sending in their answers, and then the researchers would display the results, talk about them, and answer any questions that the students might have. These discussions got the students talking about the concepts, drawing connections, and often moving beyond the specific clicker question they’d been asked.

Although there was no difference in engagement between the course’s cohorts during weeks 10 and 11, during week 12 the engagement in the deliberate-practice class was nearly double what it was in the traditional class. The researchers actually measured engagement not by how much students spoke up or answered questions, but by something more subtle: whether they were nodding and gesturing as they listened, or texting and checking Facebook (as noted by observers).   

But it was more than just engagement. The students in the class got immediate feedback on their understanding of the various concepts from both fellow students and their instructors. This allowed them to start reasoning more like physicists—by first posing appropriate questions, then figuring out which concepts were applicable, and then reasoning from those concepts to an answer.

At the end of week 12, the students in both cohorts were given a multiple-choice clicker test to see how well they had learned the material. The average score of the students in the traditional section was 41 percent; the average in the deliberate-practice class was 74 percent—a highly significant difference.

How to unlock students’ potential

Let’s take a closer look at this UBC physics class to see how the principles of deliberate practice can be applied to help students learn faster and better than they do with traditional approaches.

The first thing that Wieman and his colleagues did in designing the class was to speak with the traditional instructors to determine exactly what the students should be able to do once they finished the section. A major difference between the deliberate-practice approach and the traditional approach to learning lies with the emphasis placed on skills versus knowledge—what you can do versus what you know.

Deliberate practice is all about the skills. You pick up the necessary knowledge in order to develop the skills; knowledge should never be an end in itself. Nonetheless, deliberate practice results in students picking up quite a lot of knowledge along the way.

If you teach a student facts, concepts, and rules, those things go into long-term memory as individual pieces, and if a student then wishes to do something with them—use them to solve a problem, reason with them to answer a question, or organize and analyze them to come up with a theme or a hypothesis—the limitations of attention and short-term memory kick in. The difficulty of keeping all of these different, unconnected pieces in mind at the same time makes it nearly impossible for a student to successfully generate a solution.

But when a student learns these various facts, concepts, and rules in the context of building skills—learning to analyze and solve problems—the different pieces are naturally integrated into an interconnected network of understanding, a “mental representation” of how the various facts, images, rules, and relationships work together in a meaningful whole. This mental representation is in turn associated with other knowledge and understanding that the individual has accumulated. Now when the student is given a problem to solve, it is no longer a matter of juggling a collection of independent bits of information but rather thinking in terms of patterns of information, which the brain can do much more efficiently and effectively.

You don’t build mental representations by thinking about something or being taught by a teacher; you build them by incrementally adjusting them as you attempt to perform a relevant task with feedback. Initially you will likely fail, but as you revise your approach, trying over and over again until the task is mastered, you gradually build an accurate and effective mental representation that can be used on similar tasks in the future.

And this is what Wieman and his colleagues set out to do in the physics class. Once they had put together a list of what things their students should be able to do, they transformed it into a collection of specific learning objectives.

This is consistent with a deliberate-practice approach: When teaching everyday physics phenomena, it is necessary to get the students thinking about them based on their existing knowledge and to help the students identify mistakes and misconceptions; teachers do this by providing students with a series of problems they can eventually learn to solve correctly by getting feedback on their incorrect solutions. As they gradually adjust their mental representations, the students refine their thinking about the physical phenomena until they have a relatively effective understanding.

While this may sound similar to the scaffolding approach used in traditional education, it differs in its focus on developing effective mental representations. Specifically, the idea is to identify a target performance—namely, being able to correctly reason and predict outcomes in the real world—and then work to achieve that target performance by changing the student’s thought processes to refine the necessary mental representations at each step of the way. Then, the teacher makes sure that the student has changed their mental representations and relevant thinking before moving to more complex phenomena.

Earlier research comparing physics experts with physics students found that while traditionally trained students may sometimes be almost as good as the experts at solving quantitative problems—that is, problems involving numbers that can be solved by applying the right equation—the students were far behind the experts in their ability to solve qualitative problems, or problems that involve conceptual reasoning but without any numbers that can be plugged into memorized equations: For example, why is it hot in summer and cold in the winter? Answering a question like that requires less a command of numbers than it does a clear understanding of the concepts that underlie particular events or processes—that is, good mental representations.

To help the physics students in their class develop such mental representations, Wieman and his coworkers developed sets of clicker questions and learning tasks that would require the students to think and then provided them with immediate feedback to help them reach the learning objectives the instructors had previously identified.

Finally, the classes were structured so that the students would have the opportunity to deal with the various concepts over and over again, getting feedback that identified their mistakes and showed how to correct them. Some of the feedback came from fellow students in the discussion groups and some from the instructors, but the important thing was that the students were getting immediate responses that told them when they were doing something wrong and how to fix it.

This redesigned physics class offers a roadmap for redesigning instruction according to deliberate-practice principles:

  • Begin by identifying what students should learn how to do based on what skills experts need to do their work. The objectives should be skills, not knowledge.
  • Understand the mental representations that experts use, and provide students problem situations with feedback to help them gradually develop similar mental representations. This will involve teaching the skill by focusing on one aspect at a time, with each aspect selected by the teacher to keep students out of their comfort zone but not so far out that they cannot master that step.
  • Give plenty of repetition and feedback; the regular cycle of try, fail, get feedback, try again, and so on is how the students will build their mental representations.

At the University of British Columbia, the success of Wieman’s deliberate practice–based approach to teaching physics has led many other professors there to follow suit. According to an article in Science magazine, in the years after the experiment, deliberate-practice methods were adopted in nearly one hundred science and mathematics classes there with a total enrollment of more than thirty thousand students.

Redesigning teaching methods using deliberate practice could dramatically increase how quickly and how well students learn—as the almost unbelievable improvements in Wieman’s students indicate. And by doing so, it could help engage and encourage the students who feel like they don’t have any natural talent in science and math, or English, or the arts. Progress is motivating, and it means that the path to mastery—the path that may have seemed closed to these students—is now within reach.

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