提升可解释性知识追踪，让模型预测更贴近学生真实学习状态。
Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space
- 从学生交互数据中提取人类可理解的学习特征
- 结合学习能力特征，预测准确率提升且符合认知理论
- 适合教育AI研发者与需要透明决策的智能教学系统
知识追踪（KT）在评估学生技能掌握程度和预测未来表现方面起核心作用。尽管基于深度学习的KT模型相比传统方法具有更高的预测精度，但其复杂性和黑箱特性限制了对学习过程的心理学意义解释。这种模型参数与认知理论之间的脱节，阻碍了对学习过程的理解与优化，也降低了在教育应用中的可信度。为此，本文通过探索来自学生交互数据的人类可理解特征，增强可解释性知识追踪模型。引入反映学生学习能力等额外特征后，新方法在保持与认知理论一致性的前提下，提升了预测准确性。研究旨在平衡预测性能与可解释性，推动自适应学习系统的实际应用价值。
原文摘要 · Abstract (English)
Knowledge Tracing (KT) plays a central role in assessing students skill mastery and predicting their future performance. While deep learning based KT models achieve superior predictive accuracy compared to traditional methods, their complexity and opacity hinder their ability to provide psychologically meaningful explanations. This disconnect between model parameters and cognitive theory poses challenges for understanding and enhancing the learning process, limiting their trustworthiness in educational applications. To address these challenges, we enhance interpretable KT models by exploring human-understandable features derived from students interaction data. By incorporating additional features, particularly those reflecting students learning abilities, our enhanced approach improves predictive accuracy while maintaining alignment with cognitive theory. Our contributions aim to balance predictive power with interpretability, advancing the utility of adaptive learning systems.
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