反事实单调知识追踪用于评估学生对知识概念的动态掌握
Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts
AI总结：
针对知识追踪中概念掌握无标签导致中间值不准确的问题，提出反事实单调知识追踪（CMKT），通过反事实假设约束学生知识掌握演变，提升评估准确性。
摘要：
作为知识追踪（KT）任务的核心，评估学生对知识概念的动态掌握对于线下教学和在线教育应用都至关重要。由于学生对知识概念的掌握通常是无标签的，现有知识追踪方法依赖于从历史练习到知识概念掌握、再到学生练习反应的隐式范式，以应对概念掌握无标签的挑战。然而，纯粹预测学生反应而不对隐藏的概念掌握值施加特定约束，并不能保证这些中间值作为概念掌握值的准确性。为解决这一问题，我们提出了一种有原则的方法，称为反事实单调知识追踪（CMKT），该方法在上述隐式范式的基础上，通过使用反事实假设来约束学生对知识概念掌握的演变过程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
