Predicting Student Attrition in Competitive Programming: A Large-Scale Study Integrating Survey Insights and Global Behavioral Logs

Abstract

Competitive programming (CP) offers computer science students an environment for developing algorithmic reasoning skills. However, sustained participation remains a challenge, as many students disengage after encountering skill plateaus or performance anxiety. While educational data mining (EDM) has studied dropout in MOOCs and academic courses, CP attrition remains understudied. This paper presents a dual-layer framework combining large-scale Codeforces activity logs (n=1,816) with a multi-institutional psychographic survey across 10 universities in Bangladesh (n=64). Analysis reveals that true attrition is preceded by an 83.71% reduction in contest participation and a 15.6% increase in struggle time. We identify a"Skill-Application Paradox": stopped students self-report higher mathematical confidence (3.88 vs. 3.41) and data structure understanding (3.57 vs. 3.09) than active peers, yet their independent practice and upsolving habits are significantly weaker (p<0.001). Benchmark evaluations show that a Soft-Voting Ensemble achieves a 0.737 CV F1-score on behavioral logs, while Random Forest achieves 0.924 CV F1-score on psychographic data (an exploratory pilot). We deploy the survey-trained model as a proof-of-concept Early Warning System over 22 active students, identifying 4 at high risk. These findings show that behavioral and psychographic signals can support data-driven mentoring in computing programs.