arXiv:2608.28618 · v2
Predicting Student Attrition in Competitive Programming: A Large-Scale Study Integrating Survey Insights and Global Behavioral Logs
摘要
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.
作者备注：Accepted at International Conference on Emerging Frontiers in Advanced Sciences and Technologies (EFAST 2026). 10 pages, 15 figures, 2 tables