Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning

This paper proposes using TabDDPM, a diffusion-based generative AI model, to create synthetic student learning records that augment training data for Deep Knowledge Tracing (DKT) systems used in personalized adaptive learning. The approach is validated on ASSISTments datasets to demonstrate improved DKT performance, particularly when training data is limited.

In contrast to pedagogies like evidence-based teaching, personalized adaptive learning (PAL) distinguishes itself by closely monitoring the progress of individual students and tailoring the learning path to their unique knowledge and requirements. A crucial technique for effective PAL implementation is knowledge tracing, which models students' evolving knowledge to predict their future performance. Based on these predictions, personalized recommendations for resources and learning paths can be m

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