Do We Fully Understand Students' Knowledge States? Identifying and Mitigating Answer Bias in Knowledge Tracing

This paper addresses answer bias in knowledge tracing (KT) systems, which monitor students' evolving knowledge states through their interactions with concept-related questions. The authors propose a counterfactual reasoning framework (CORE) that helps KT models better understand actual student knowledge rather than memorizing question-specific answer patterns.

Knowledge tracing (KT) aims to monitor students' evolving knowledge states through their learning interactions with concept-related questions, and can be indirectly evaluated by predicting how students will perform on future questions. In this paper, we observe that there is a common phenomenon of answer bias, i.e., a highly unbalanced distribution of correct and incorrect answers for each question. Existing models tend to memorize the answer bias as a shortcut for achieving high prediction perf

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