Abstract

Deep Knowledge Tracing (DKT), powered by recurrent neural networks (RNNs) and their advanced variants, effectively captures complex learner progression patterns. By uncovering latent structures in learning behaviors, DKT enables precise predictions and facilitates personalized interventions, enhancing the effectiveness of intelligent educational platforms. However, real-world applications often face challenges due to limited annotated learning interactions for model training, constrained by factors such as budget restrictions and privacy concerns. To address the challenges of low-resource DKT, this study integrates semi-supervised learning and ensemble deep learning. The approach begins by training multiple DKT models, including Knowledge Proficiency Tracing (KPT), Exercise-Correlated Knowledge Proficiency Tracing (EKPT), and Dynamic Key-Value Memory Networks (DKVMN), using a combination of limited labeled learning interactions and a large volume of unlabeled learning interactions. These models are then ensembled through majority voting, effectively leveraging the strengths of both semi-supervised learning and ensemble strategies. Experimental results on benchmark datasets, such as ASSISTments, demonstrate that the proposed model enhance the performance for the low-resource DKT, significantly improving key metrics including AUC, accuracy, and precision.