True attrition in competitive programming is preceded by an 83.71% reduction in monthly contest participation¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Active Codeforces users participated in far more contests per month (7.27 vs 1.19) than true attrition users, the strongest single behavioral signal of impending disengagement. → Azuad Islam Ruhan 2026

Evidence¶

Azuad Islam Ruhan 2026¶

Azuad Islam Ruhan, Golam Mostofa Naeem, Rakibul Islam Rafi, Sherin Afrin Mim, Nazira Bani Opi, Dewan Fahad Chowdhury, and Md. Rejaul Korim Sadi. (2026). Predicting Student Attrition in Competitive Programming: A Large-Scale Study Integrating Survey Insights and Global Behavioral Logs. https://arxiv.org/abs/2608.28618 doi:10.48550/arxiv.2608.28618

q2 · i? · associational · r1

Observational comparison of 908 active and 908 true attrition users from a balanced Codeforces log dataset (drawn from 6,927 profiles), using an independent samples t-test. The article reports "an 83.71% reduction" in contest participation, p <0.001, calling it "the strongest single signal of impending disengagement."

"Active users participated in an average of 7.27 contests per month, compared to just 1.19 for true attrition users—an 83.71% reduction (p <0.001, independent samples t-test)."

Discussion¶

Related Claims¶

• Engineered practice-balance features outperform raw activity counts: the Intensity Ratio ranks third among Codeforces predictors, and upsolving habit dominates the survey model — related
• Mean struggle time before acceptance is 15.6% higher among true attrition users but does not reach statistical significance — related
• A Soft-Voting Ensemble achieves the best behavioral-dataset attrition prediction (CV F1 = 0.737, test recall = 0.769) — related
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