预测竞赛编程中的学生流失：结合调查洞察与全球行为日志的大规模研究
Predicting Student Attrition in Competitive Programming: A Large-Scale Study Integrating Survey Insights and Global Behavioral Logs
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中文总结 AI 辅助
本研究结合Codeforces行为日志与多校心理调查，发现竞赛编程流失前的关键信号，提出双层框架训练模型，构建预警系统识别高风险学生，为计算课程指导提供数据支撑。
中文摘要 AI 辅助
竞赛编程（CP）为计算机科学学生提供了培养算法推理能力的环境，但持续参与仍是挑战，许多学生在遇到技能瓶颈或表现焦虑后会退出。教育数据挖掘（EDM）已研究了大规模开放在线课程（MOOC）和学术课程中的辍学问题，但竞赛编程流失问题仍研究不足。本文提出双层框架，结合Codeforces的大规模活动日志（n=1816）与孟加拉国10所高校的多机构心理调查（n=64）。分析显示，真正的流失前会出现竞赛参与度降低83.71%、挣扎时间增加15.6%的情况。研究发现“技能应用悖论”：停止参与的学生自我报告的数学自信（3.88 vs. 3.41）和数据结构理解（3.57 vs. 3.09）高于活跃学生，但独立练习和补题习惯显著更弱（p<0.001）。基准评估显示，软投票集成模型在行为日志上的交叉验证F1分数为0.737，随机森林模型在心理数据上的交叉验证F1分数为0.924（探索性试点）。研究将经调查训练的模型作为概念验证预警系统部署在22名活跃学生中，识别出4名高风险学生。这些发现表明，行为和心理信号可支持计算课程中的数据驱动指导。
英文摘要
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.
发表机构
- Department of Computer Science and Engineering, Metropolitan University（孟加拉国锡尔赫特大都会大学计算机科学与工程系）
机构由 AI 辅助整理，请以论文原文为准。
