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This content will become publicly available on January 1, 2026

Title: Auto-Grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
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.  more » « less
Award ID(s):
2111305
PAR ID:
10620958
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
SCITEPRESS - Science and Technology Publications
Date Published:
ISBN:
978-989-758-746-7
Page Range / eLocation ID:
356 to 363
Format(s):
Medium: X
Location:
Porto, Portugal
Sponsoring Org:
National Science Foundation
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