Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space
This work addresses the need for more trustworthy and interpretable adaptive learning systems in education, though it appears incremental by building on existing interpretable KT models.
The paper tackles the problem of improving interpretability in knowledge tracing models for student performance prediction by incorporating human-understandable features, resulting in enhanced predictive accuracy while aligning with cognitive theory.
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.