A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings
This addresses the challenge of incremental knowledge tracing for students in data-scarce educational environments, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of knowledge tracing in low-resource classroom settings by proposing a hierarchical probabilistic framework that models student understanding over a tree of knowledge concepts, resulting in consistent outperformance over baselines in online scenarios.
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.