基于知识树结构的动态学习追踪模型，适合数据少的课堂场景
A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings
- 用树状知识结构建模学生掌握情况，利用层次先验信息增强低资源表现
- 在真实低数据在线场景中，预测准确率显著优于现有方法
- 适合教育系统实时更新学生能力评估，尤其适用于数据不足的课堂
知识追踪（KT）旨在根据学生的历史作答记录，估计其知识状态并预测新题表现。许多真实课堂场景中数据稀疏且需随学生答题历史持续更新，这对现有方法带来挑战。为应对低资源环境，本文重新利用课堂中常见的分层知识概念（KC）信息，作为数据稀疏时的强先验。为此，提出基于知识树的知识追踪框架KT²，采用隐马尔可夫树模型对知识概念的层次结构进行建模，通过EM算法估计学生掌握程度，并支持新答题数据到达时的增量更新。实验表明，KT²在真实的在线、低资源场景下持续优于多个强基线模型。
原文摘要 · Abstract (English)
Knowledge tracing (KT) aims to estimate a student's evolving knowledge state and predict their performance on new exercises based on performance history. Many realistic classroom settings for KT are typically low-resource in data and require online updates as students' exercise history grows, which creates significant challenges for existing KT approaches. To restore strong performance under low-resource conditions, we revisit the hierarchical knowledge concept (KC) information, which is typically available in many classroom settings and can provide strong prior when data are sparse. We therefore propose Knowledge-Tree-based Knowledge Tracing (KT$^2$), a probabilistic KT framework that models student understanding over a tree-structured hierarchy of knowledge concepts using a Hidden Markov Tree Model. KT$^2$ estimates student mastery via an EM algorithm and supports personalized prediction through an incremental update mechanism as new responses arrive. Our experiments show that KT$^2$ consistently outperforms strong baselines in realistic online, low-resource settings.
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