A time for telling

by Stephanie Chasteen on October 10, 2008

“A Time for Telling” is the title of one of my favorite papers of Dan Schwartz (Professor of Education at Stanford). In it, he argues that lecture isn’t all bad. We complain that lecture (or “direct instruction” in ed-speak) doesn’t result in a lot of learning for our students. This has been shown again and again, in a lot of studies. But it’s pretty hard to completely eradicate lecture from our universities (or high schools, etc.) — it’s a pretty efficient way of communicating information. But if students first struggle with the ideas and concepts, then they’re prepared to learn from it. This is called Preparation for Future Learning.

For example, you could imagine (and it’s been shown) that students who first invent the idea of density (by being given the task of coming up with a way to describe how many clowns there are per square foot at a circus) will be better able to answer a question about the density of water than, say, a student who was just given the formula for density and shown a worked problem using gold. And a recent study by Schwartz shows just that, that those students who first invented the solution were better able to transfer the idea to a new situation. He writes:

Direct instruction is important, because it delivers the explanations and efficient solutions invented by experts. To gain this benefit without undermining transfer, direct instruction can happen after students have engaged the deep structure, per the Invent condition. [The students who invented the solution on their own] performed just as well on a subsequent test of word problems about density and speed. Direct instruction becomes problematic when it shortcuts the appreciation of deep structure. Across conditions, students who encoded the deep structure of the clown problems were twice as likely to transfer. It is just that fewer students in the Tell-and-Practice condition encoded the deep structure, because they had received direct instruction too soon.

Similarly, he later cites a study that found:

For example, college students learn more from lectures and readings when they first work with relevant data compared to when they write a summary of a chapter that explains the same data .

In some instances, he says, it is useful to just receive direct instruction because the goal is to build rote, routine skills. But in math and science, this isn’t the case:

In math and science, instruction cannot exhaust all possible situations. Transfer and adaptation are important. Although automaticity is important for some facts such as “2 x 3 = 6,” real situations rarely come with formulas attached, so students need to learn to recognize the relevant deep structures. Moreover, the cumulative curricula of math and science mean that students should build a base of knowledge on deep structures from which future learning can grow and adapt.

But teaching this way brings up the problem of assessment:

In the current milieu of high-stakes testing, standardized assessments largely measure routine expertise; namely, efficient recapitulation. If educators want students to become adaptive, innovative citizens who keep learning through changing times, current assessments do not fit. A better fit would map students’ trajectory towards adaptive expertise. Ideally, assessments would examine students’ ability to transfer, particularly for new learning. Such assessments would include resources for learning during the test (for example, a simulation that students can freely manipulate).

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