Disentangled Knowledge Tracing for Alleviating Cognitive Bias

This paper proposes DisKT, a Knowledge Tracing model for Intelligent Tutoring Systems that addresses cognitive bias (underload/overload) caused by unbalanced question distributions, using causal inference to separately model students' familiar and unfamiliar abilities for more accurate knowledge state assessment and personalized exercise recommendations.

In the realm of Intelligent Tutoring System (ITS), the accurate assessment of students' knowledge states through Knowledge Tracing (KT) is crucial for personalized learning. However, due to data bias, i.e., the unbalanced distribution of question groups ( e.g., concepts), conventional KT models are plagued by cognitive bias, which tends to result in cognitive underload for overperformers and cognitive overload for underperformers. More seriously, this bias is amplified with the exercise recommen

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