Decomposing the Doer Effect in Programming Practice: Code Writing Stands Out Among Active Practice

2026-08-03

AI Summary

This study investigates the impact of different forms of active practice—namely code writing, code tracing, code completion, visualization, and explanation—on learning outcomes in programming education, testing the applicability of the "doer effect" in computer science contexts. Analyzing log data from 334 students across 11 semesters using an interactive programming platform, the authors employ statistical modeling to examine associations between practice types and post-test performance. For the first time in programming education, active practice is disaggregated by type, revealing that only code writing exhibits a statistically significant positive relationship with achievement. Overall, active practice yields 3.2 times the learning gain of passive activities, yet other practice forms do not reach significance. These findings suggest that not all active learning strategies are equally effective, and that high-challenge, feedback-rich code writing holds the greatest instructional value.

Abstract

The "doer effect" suggests that actively doing practice activities is more strongly associated with learning outcomes than passively viewing content. In the doer effect literature, "doing" refers specifically to active practice. However, this categorization treats different forms of active practice as equivalent, leaving open whether some types of active practice are more effective than others. In this paper, we investigate whether the doer effect extends to computer science instruction and whether some forms of doing stand out compared to other forms. We analyze log data from 334 students across 11 semesters of introductory and intermediate Java who used an interactive practice system with five content types: Code Writing, Code Tracing, Code Completion, Code Visualizations, and Code Explanations. Consistent with prior doer effect work, we find that active practice activities were associated with 3.2 times better learning outcomes than passive activities. Interestingly, among the active practice, code writing was the most strongly associated with improved posttest performance, while no other activity type showed a comparable association. These results highlight the importance of challenging, feedback-supported practice activities, such as code writing problems.

Problem

• doer effect
• active practice
• code writing
• learning outcomes
• programming education

Innovation

• doer effect
• code writing
• active practice
• programming education
• learning outcomes