Imbalanced Data for Knowledge Tracing
J. Chen, I‐Wei Lai · 2023
In the realm of Intelligent Tutoring Systems (ITS), Knowledge Tracing (KT) techniques play a vital role in tracking and assessing student progress and understanding of a subject. However, in practice various data classes are generally collected in a way of underrepresentation, leading to the KT performance degradation. In this work, we propose a data deduplication technique to balance the inputs to improve the KT performance. Our experimental results confirm the efficacy of the proposed scheme in addressing imbalanced data and improving KT performance.