arXiv cs.CY· Azuad Islam Ruhan, Md. Rejaul Korim Sadi, Rakibul Islam Rafi, Dewan Fahad Chowdhury, Nazira Bani Opi, Sherin Afrin Mim, Golam Mostofa Naeem·· 10 小时前AI 评分20
基于调查洞察与全球行为日志的大规模研究：预测竞赛编程中的学生流失
Predicting Student Attrition in Competitive Programming: A Large-Scale Study Integrating Survey Insights and Global Behavioral Logs
AI 导读
一项大规模研究对比了 Codeforces 上流失用户与同水平活跃用户的行为，从 6,927 个账户中筛选出 3,421 个活跃账户和 908 个至少三个月未提交的流失账户（峰值评分均低于 1500）。
正文 · 原文
Abstract:We compared the recorded behavior of competitive programmers who became inactive on Codeforces with that of active users of similar skill. From 6,927 accounts collected through the public Codeforces application programming interface in June 2026, we removed 888 accounts whose retrieved submission histories were incomplete and restricted both groups to a peak rating below 1500. This left 3,421 active accounts and 908 accounts with no submission for at least three months, labeled attrition. The differences between the groups were small. Accounts labeled attrition accepted smaller shares of their attempts, overall and on mathematics and data-structure problems (rank-biserial r from -0.16 to -0.12), and solved fewer problems per rated contest (median 5.95 versus 7.80; r = -0.13); struggle time, upsolving and recovery after rating drops did not differ after correction for multiple testing. Two pre-specified models using eight features that do not depend on the length of the observation window reached cross-validated areas under the receiver operating characteristic curve of 0.596 (logistic regression) and 0.611 (random forest), with probability estimates barely better than the base rate. Across random samples, stricter cleaning rules, other definitions of inactivity and matching on account tenure, the value ranged from 0.58 to 0.66. Because the features summarize each user's whole recorded history, these results describe differences between groups and do not show that inactivity can be predicted for individual users. The data and code are publicly archived, and one command regenerates every reported number.
| Comments: | Accepted at International Conference on Emerging Frontiers in Advanced Sciences and Technologies (EFAST 2026). 10 pages, 15 figures, 2 tables |
| Subjects: | Computers and Society (cs.CY) |
| ACM classes: | K.3.2; I.2.6 |
| Cite as: | arXiv:2608.28618 [cs.CY] |
| (or arXiv:2608.28618v2 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2608.28618 arXiv-issued DOI via DataCite |
Submission history
From: Md. Rejaul Korim Sadi [view email]
[v1]
Mon, 27 Jul 2026 10:00:08 UTC (792 KB)
[v2]
Mon, 9 Nov 2026 20:20:30 UTC (17 KB)
来源：arXiv cs.CY · arxiv.org