Secondary outcomes differed in predictability: near-term related-task performance was hardest while 7–28-day performance showed a longitudinal signal¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r1–r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Near-term related-task performance was the most challenging prediction target, with test ROC-AUC of 0.303 for the dedicated fusion GRU, 0.433 for logistic regression, and 0.383 for gradient boosting. → Shihao Wang 2026 q2 i? The 7–28-day related-task outcome showed promising longitudinal discrimination, with dedicated logistic regression and gradient boosting achieving ROC-AUC values of 0.691 and 0.699 across 67 test cases from 25 students. → Shihao Wang 2026 (2)

Evidence¶

Shihao Wang 2026¶

Shihao Wang. (2026). A Risk-Adaptive and Evidence-Constrained Framework for Generative AI Feedback in Programming Education. https://arxiv.org/abs/2609.29874 doi:10.48550/arxiv.2609.29874

q2 · i? · associational · r2

Dedicated models on 209 test states for near-term related-task performance. "Near-term related-task performance was the most challenging prediction target", with test ROC-AUC of 0.303, 0.433, and 0.383 across the three model types.

"Near-term related-task performance was the most challenging prediction target. The dedicated fusion GRU achieved validation PR-AUC 0.608 and test ROC-AUC 0.303; logistic regression reached ROC-AUC 0.433, and gradient boosting reached 0.383."

Shihao Wang 2026 (2)¶

Shihao Wang. (2026). A Risk-Adaptive and Evidence-Constrained Framework for Generative AI Feedback in Programming Education. https://arxiv.org/abs/2609.29874

q2 · i? · associational · r1

Longitudinal related-task outcome analysis on 67 test cases from 25 students. "Dedicated logistic regression and gradient boosting achieved ROC-AUC values of 0.691 and 0.699" with wide student-cluster intervals; the authors call for further longitudinal validation with larger samples.

"Dedicated logistic regression and gradient boosting achieved ROC-AUC values of 0.691 and 0.699, respectively , with student-cluster intervals extending from approximately 0.49 to 0.85."

Discussion¶

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