Enhanced personalized learning exercise question recommendation model based on knowledge tracing

This paper proposes a personalized exercise question recommendation model that uses graph convolutional neural networks and bidirectional GRU for knowledge tracing to predict student performance and recommend appropriately difficult exercises. The model is evaluated on ASSISTment datasets containing K-12 student interaction records, achieving 90.8% and 92.6% accuracy in predicting student responses.

Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind

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