Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts
This work addresses the challenge of unlabeled concept mastery for educational applications, but it appears incremental as it builds on an existing implicit paradigm.
The paper tackles the problem of accurately assessing students' dynamic mastery of knowledge concepts in Knowledge Tracking by addressing the lack of constraints on hidden mastery values in existing methods, proposing CMKT which uses a counterfactual assumption to improve accuracy.
As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.