Using Programming Process Data to Detect Differences in Students' Patterns of Programming

Abstract

Analyzing the process data of students as they complete programming assignments has the potential to provide computing educators with insights into their students and the processes by which they learn to program. In prior work, we developed a statistical model that accurately predicts students' homework grades. In this paper, we investigate the relationship between the paths that students take through the programming states on which our statistical model is based, and their overall course achievement. Examining the frequency of the most common transition paths revealed significant differences between students who earned A's, B's, and C's in a CS 2 course. Our results indicate that a) students of differing achievement levels approach programming tasks differently, and b) these differences can be automatically detected, opening up the possibility that they could be leveraged for pedagogical gain.