Taking Advantage of Scale by Analyzing Frequent Constructed-Response, Code Tracing Wrong Answers

Abstract

Constructed-response, code-tracing questions ("What would Python print?") are good formative assessments. Unlike selected-response questions simply marked correct or incorrect, a constructed wrong answer can provide information on a student's particular difficulty. However, constructed-response questions are resource-intensive to grade manually, and machine grading yields only correct/incorrect information. We analyzed incorrect constructed responses from code-tracing questions in an introductory computer science course to investigate whether a small subsample of such responses could provide enough information to make inspecting the subsample worth the effort, and if so, how best to choose this subsample. In addition, we sought to understand what insights into student difficulties could be gained from such an analysis. We found that ~5% of the most frequently given wrong answers cover ~60% of the wrong constructed responses. Inspecting these wrong answers, we found similar misconceptions as those in prior work, additional difficulties not identified in prior work regarding language-specific constructs and data structures, and non-misconception "slips" that cause students to get questions wrong, such as syntax errors, sloppy reading/writing. Our methodology is much less time-consuming than full manual inspection, yet yields new and durable insight into student difficulties that can be used for several purposes, including expanding a concept inventory, creating summative assessments, and creating effective distractors for selected-response assessments.