Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges

TL;DR

This observational study across five community colleges demonstrates that frequent engagement with auto-grader feedback correlates with higher student scores and improved assignment quality in introductory Python courses.

Contribution

It provides empirical evidence on the positive impact of auto-grader feedback utilization on student performance, filling a gap in existing research.

Findings

Frequent feedback checking correlates with higher scores.

Following feedback leads to better assignment outcomes.

Auto-grader feedback is effective in improving learning.

Abstract

Automated grading systems, or auto-graders, have become ubiquitous in programming education, and the way they generate feedback has become increasingly automated as well. However, there is insufficient evidence regarding auto-grader feedback's effectiveness in improving student learning outcomes, in a way that differentiates students who utilized the feedback and students who did not. In this study, we fill this critical gap. Specifically, we analyze students' interactions with auto-graders in an introductory Python programming course, offered at five community colleges in the United States. Our results show that students checking the feedback more frequently tend to get higher scores from their programming assignments overall. Our results also show that a submission that follows a student checking the feedback tends to receive a higher score than a submission that follows a student ignoring the feedback. Our results provide evidence on auto-grader feedback's effectiveness, encourage their increased utilization, and call for future work to continue their evaluation in this age of automation