探究知识追踪模型在学生概念漂移下的鲁棒性
Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
AI总结：
针对在线学习平台中学生概念漂移问题，评估四种知识追踪模型在五个学年数据上的表现，发现BKT模型鲁棒性最强，而基于注意力的复杂模型性能下降更快。
摘要：
知识追踪（KT）几十年来一直是教育数据挖掘领域的经典问题，通常假设其建模的底层学习过程保持静态。鉴于在线学习平台（OLP）不断变化的格局，我们通过测试模型在单个学年及多个学年内的表现，研究了概念漂移和不断变化的学生群体如何影响OLP内的学生行为。我们将四种被广泛研究的KT模型应用于五个学年的数据，以评估KT模型对概念漂移的敏感性。通过分析，我们发现所有四类KT模型都会表现出性能下降；在应用于较新数据时，贝叶斯知识追踪（BKT）仍是最稳定的KT模型，而更复杂的、基于注意力的模型失去预测能力的速度要快得多。
英文摘要：
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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