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).

翻译：知识追踪（KT）旨在根据学生的历史答题行为预测其对练习的响应。然而，当前大多数KT方法侧重于提升整体AUC，在建模过长或过短序列的优化方面仍有较大空间。随着序列长度增加，计算成本将呈指数级增长。因此，KT方法通常将序列截断至可接受长度，这使得在线服务系统中的模型难以捕捉具有过长序列的学生的完整历史练习行为。相反，使用大多数KT方法建模练习序列较短的学生，可能因观测样本有限而导致过拟合。为了解决上述局限性，我们提出一种名为“序列灵活知识追踪”（SFKT）的模型。