Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems

This paper proposes LBKT, a hybrid LSTM-BERT model for Knowledge Tracing in Intelligent Tutoring Systems that predicts student performance on subsequent tasks by analyzing long sequences of historical interaction data. The model uses Rasch model-based embeddings to handle question difficulty and demonstrates improved accuracy, speed, and interpretability on benchmark ITS datasets.

The field of Knowledge Tracing aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed Knowledge Tracing models that use data from Intelligent Tutoring Systems to predict students' subsequent actions. However, with the development of Intelligent Tutoring Systems, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based Knowledge Tracing models face

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