不遗漏任何长度：增强知识追踪以建模过长或过短序列
No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
AI总结：
针对知识追踪中过长序列计算成本高和过短序列易过拟合的问题，提出序列灵活知识追踪模型SFKT，以灵活建模不同长度序列。
摘要：
知识追踪（KT）旨在根据学生历史答题行为预测其对练习的反应。然而，当前大多数KT方法侧重于提高整体AUC，在建模过长或过短长度的序列方面仍有很大的优化空间。随着序列变长，计算成本将呈指数增长。因此，KT方法通常将序列截断到可接受的长度，这使得在线服务系统上的模型难以捕获序列过长的学生的完整历史练习行为。相反，使用大多数KT方法对练习序列较短的学生进行建模，可能因观测样本有限而导致过拟合。为解决上述局限，我们提出了一种名为序列灵活知识追踪（SFKT）的模型。
英文摘要：
Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
