研究在线学习中学生知识变化对模型的影响，发现复杂模型易失效。
Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
- 用四个经典模型在五年数据上测试知识追踪稳定性
- 所有模型性能随时间下降，注意力模型退化最快
- 贝叶斯知识追踪在新数据中仍最稳定，适合长期使用
知识追踪（KT）是教育数据挖掘中的经典问题，通常假设学习过程保持不变。随着在线学习平台（OLPs）的快速发展，我们研究了概念漂移和学生群体变化对平台内学生行为的影响，通过在单个学年及跨多个学年测试模型表现。将四种成熟的KT模型应用于五年的数据，评估其对概念漂移的敏感性。分析显示，所有四类模型均表现出性能下降；贝叶斯知识追踪（BKT）在新数据上仍保持最稳定，而基于注意力的复杂模型预测能力显著更快衰退。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) has been an established problem in the educational data mining field for decades, and it is commonly assumed that the underlying learning process being modeled remains static. Given the ever-changing landscape of online learning platforms (OLPs), we investigate how concept drift and changing student populations can impact student behavior within an OLP through testing model performance both within a single academic year and across multiple academic years. Four well-studied KT models were applied to five academic years of data to assess how susceptible KT models are to concept drift. Through our analysis, we find that all four families of KT models can exhibit degraded performance, Bayesian Knowledge Tracing (BKT) remains the most stable KT model when applied to newer data, while more complex, attention based models lose predictive power significantly faster.
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