Schools must go beyond surface-level learning, and better tutoring can help

In the mid-1970s, Ference Marton and Roger Säljö of the University of Gothenburg in Sweden noticed that their students took different approaches to learning. Some focused on remembering information, others on understanding it: connecting it to other information, figuring out when it might be useful, and so on. Marton and Säljö christened the former surface learning and the latter deep learning.

Reading the last paragraph, you may have already formed the opinion that surface learning is bad and deep learning is good. But it’s not that simple. Not all content is deep. Knowing your multiplication facts, for example, does not require much conceptual depth, it’s all surface. And deep learning relies on knowledge of surface details. You can’t construct an argument integrating multiple causes of World War II if you can’t remember any of them.

So you need both; problems arise if surface learning is all you do. If math instruction consists of tips and tricks such as dividing fractions by flipping and multiplying, students will gain only a surface-level understanding. Conceptual understanding is harder to serve up, with the result that it may simply be absent for several grades. And so, if and when you at last arrive at calculus, the wheels come off.

This isn’t just a concern in mathematics. Whatever the topic, traveling beyond beginner levels and into the realm of experts requires depth. It has, though, proven difficult to induce students to take a deep approach. For instance, since the majority of assessments operate at a surface level, the prospect of being tested signals to students that they only need to memorize rather than to understand.

Changes, therefore, are needed. Deeper learning requires better teaching—and therefore better teacher training, better curriculum, and higher standards. But that’s a heavy and long lift, and in the meantime, tutoring may offer a solution.

We know tutoring works, but does it work for both surface- and deep-level understanding? Micki Chi, a professor at the Institute for the Science of Teaching and Learning who has studied tutoring closely, designed an experiment to find out. In particular, she wanted to know what kinds of tutor moves produced deep learning.

In her experiment, tutors taught the human circulatory system. Students then answered questions such as “What does the heart do?” which requires only surface learning, since the topic was explicitly covered by the tutor. But to answer “Why is your right ventricle less muscular than your left ventricle?” requires deep learning because students have to connect the question to information about where left and right ventricles pump blood. That information was covered by the tutor but not linked to ventricle size.

Chi found that there was far less deep learning than surface, and that what deep learning there was could be explained almost entirely by a student’s prior knowledge and reading ability. In other words, tutoring rarely produces deep learning for those who need it most.

Why? One answer may be that tutors often focus on stepping through a problem, so that the learner will be able to repeat the performance on a similar problem, but may gain little insight into what is actually going on. Another possibility is tutors’ tendency to jump, at the first sign of student missteps, into long, unsolicited explanations that, the literature shows, don’t reliably lead to learning. “Most tutors,” says Chi, “just won’t shut up.”

It’s also likely that tutors just don't know how to induce deep learning. Indeed, researchers have been unable to find a reliable method for inducing deep learning—until recently. It came from a surprising finding. When researchers tracked tutee emotional states during tutoring, the most common emotion displayed was confusion. Sidney D’Mello, a professor at the University of Colorado, Boulder, who has studied people working on complex scientific concepts, says “confusion reigns supreme during deep learning activities.” And confusion was the only emotion that significantly predicted learning. Not even student engagement could match it.

This led to a deep tutoring method that would be familiar to any Hollywood screenplay writer. The secret to deep learning, like the secret to a good story, is (1) a conflict or impasseresulting in confusion, followed by (2) a resolution. In learning, as in a good story, you do not want to rush either of them. The impasse has to feel like a genuine impasse, which it won’t if it is not properly established or if, from an abundance of eagerness, it gets resolved too quickly. The resolution, when it comes, has to come from the actions of the main character. In learning, the main character is the student.

Here is an example. A student is trying to make sense of a graph of a bicycle journey on a chart of distance versus time. At one part of the chart, the line is horizontal before climbing again.

Tutor: What is going on in this flat part?

Student: I think it means the road flattened out a bit. Then they went up another hill.

The student is reading the line as an illustration, not a graph. This is a common misconception. Most tutors would launch into an explanation of the correct reading. That may lead to surface learning—“flat means no distance traveled”—but without deeper understanding, that approach will draw the student into trouble when they read other graphs, such as those that plot velocity versus time.

Stop reading for a moment and think what you might do instead if you were the tutor. How can you create an impasse here—a way for the student to realize their answer cannot be true.

Here’s one possibility:

T: Can you tell me from the graph how far they travel between those two points?

Even if the student needs help reading the distance axis to answer that, they will realize something is amiss with their earlier answer. The tutor will be tempted to jump in again with an explanation, but it’s far more powerful to let the student find their own resolution and congratulate them when they do.

S: OK… So from here to here…that’s zero distance.

T: (Silence)

S: So…they didn’t move.

T: Right. So what do you think is happening?

S: They stopped, I guess. Maybe they went to the bathroom?

T: Awesome! Who knew these charts could tell you about a bathroom visit?

You can feel the new insight scratching like a pet at the door. Do we have deep learning here? Not yet. The student doesn’t truly understand what the graph is telling them about this journey in a way that would allow them to read other graphs. They may stumble with the very next graph they see. But a new track has been etched in their brain, ready to be deepened.

Coming up with impasse-generating questions in the moment is challenging. A good fallback that works in almost every case is to say “Are you sure?” In fact, that’s a good move even when the student is correct.

Perhaps this approach—what D’Mello calls “intentionally perplexing learners”—feels uncomfortable. And tutors may find the resulting conversation takes them way off their plan for the session. But that would be to miss the true value of tutoring. The goal is not covering—moving diligently through a set course of material—but uncovering—creating moments that reveal a student’s thinking and where it can be advanced.

Editor's note: This article is based on The Science of Tutoring.

Originally published in Thomas B. Fordham Institute Flypaper.

Laurence Holt has spent the last two decades leading innovation teams in for-profit and nonprofit K-12 organizations and is a Senior Advisor at XQ Institute.