Immediate post-feedback improvement is near zero on average and does not differ significantly across feedback conditions¶

Claim · All claims
Evidence · 1 study · associational r2 · q3
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q3 i? Mean change in pass rate between consecutive submissions was near zero across all groups, with no significant differences across conditions (Wald test, p = 0.50). → Hasnain Heickal and Andrew Lan 2026

Evidence¶

Hasnain Heickal and Andrew Lan 2026¶

Hasnain Heickal and Andrew Lan. (2026). A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming. https://arxiv.org/abs/2606.08807 doi:10.48550/arxiv.2606.08807

q3 · i? · associational · r2

Descriptive and regression analysis of Δ pass rate over valid submission transitions in the classroom dataset. Fewer than 35% of transitions resulted in positive gains, and the large standard deviation reflected substantial heterogeneity in short-term responses.

"We fit a clustered logistic regression predicting improvement (∆>0 ) with standard errors clustered at the student level, and find no significant differences across conditions (Wald test,p= 0.50 )."

Discussion¶

Related Claims¶

• Feedback improves learning by a medium amount on average, but its effect varies widely with the information it carries, and a substantial share of feedback effects are negative — the general claim this finding bears on, with the studies behind it
• Prompting-based student simulators maintain artificially high pass rates (~80%) from the first submission step, while fine-tuned models track real students' low-start, sharp-increase trajectories — related
• Students teaching the ChatGPT agent passed the online judge with fewer code submission attempts than control students — related
• Submissions receiving valid LLM-generated test-case feedback had higher pass rates, but valid test cases were not associated with fewer subsequent submissions — related
• Initial prompting method did not explain immediate success on the third problem (P3) — related
• GPT-generated feedback passes simulated-student helpfulness evaluations only about 35% of the time on real student data — related
• Providing corrective feedback on practice tests does not significantly change the testing effect (g = 0.63 with feedback vs. g = 0.60 without) — related
• Feedback type shifts engagement by question type: educator feedback yields more MCQ submissions while AI feedback trends toward more open-ended revisions, with no overall difference — related
• Effects of AI-generated test case feedback depend critically on feedback validity — related
• Baseline-to-final performance change was centered near zero with a mean of −1.83 — related