A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings
By: Xinyi Gao , Qiucheng Wu , Yang Zhang and more
Potential Business Impact:
Helps students learn better with less data.
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.
Similar Papers
Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space
Computers and Society
Helps learning programs understand how students learn.
AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
Machine Learning (CS)
Improves learning systems by predicting student knowledge better.
Investigating the Robustness of Knowledge Tracing Models in the Presence of Student Concept Drift
Machine Learning (CS)
Helps online learning systems adapt to student changes.