Our recent work is built on the documented research that students often misunderstand a programming problem statement and hence “solve” the wrong problem. This not only creates frustration and wastes time, it also robs them of whatever learning objective motivated the task.
To address this, the Examplar system asks students to first write examples. These examples are evaluated against wheats (correct implementations) and chaffs (buggy implementations). Examples must pass the wheat, and correctly identify as wrong as many chaffs as possible. Prior work explores this and shows that it is quite effective.
However, there’s a problem with chaffs. Students can end up spending too much time catching them, and not enough time on the actual programming task. Therefore, you want chaffs that correspond to the problem misconceptions students are most likely to have. Having a small number of very effective chaffs is far more useful than either a large number or ineffective ones (much less both). But the open question has always been, how do we obtain chaffs?
Previously, chaffs were created by hand by experts. This was problematic because it forces experts to imagine the kinds of problems students might have; this is not only hard, it is bound to run into expert blind spots. What other method do we have?
This work is based on a very simple, clever observation: any time an example fails a wheat, it may correspond to a student misconception. Of course, not all wheat failures are misconceptions! It could be a typo, it could be a basic logical error, or it could even be an attempt to game the wheat-chaff system. Do we know in what ratio these occur, and can we use the ones that are misconceptions?
This paper makes two main contributions:
It shows that that many wheat failures really are misconceptions.
It uses these misconceptions to formulate new chaffs, and shows that they compare very favorably to expert-generated chaffs.
Furthermore, the work spans two kinds of courses: one is an accelerated introductory programming class, while the other is an upper-level formal methods course. We show that there is value in both settings.
This is just a first step in this direction; a lot of manual work went into this research, which needs to be automated; we also need to measure the direct impact on students. But it’s a very promising direction in a few ways:
It presents a novel method for finding misconceptions.
It naturally works around expert blind-spots.
With more automation, it can be made lightweight.
In particular, if we can make it lightweight, we can apply it to settings—even individual homework problems—that also manifest misconceptions that need fixing, but could never afford heavyweight concept-inventory-like methods for identifying them.
You can learn more about the work from the paper.