Measuring the effectiveness of error messages designed for novice programmers

Abstract

Good error messages are critical for novice programmers. Re-cognizing this, the DrRacket programming environment provides a series of pedagogically-inspired language subsets with error messages customized to each subset. We apply human-factors research methods to explore the effectiveness of these messages. Unlike existing work in this area, we study messages at a fine-grained level by analyzing the edits students make in response to various classes of errors. We present a rubric (which is not language specific) to evaluate student responses, apply it to a course-worth of student lab work, and describe what we have learned about using the rubric effectively. We also discuss some concrete observations on the effectiveness of these messages.