  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 obstacles such as low efficiency,
low accuracy, and low interpretability when dealing with large-scale datasets
containing long-sequence data. To address these issues and promote the
sustainable development of Intelligent Tutoring Systems, we propose a LSTM
BERT-based Knowledge Tracing model for long sequence data processing, namely
LBKT, which uses a BERT-based architecture with a Rasch model-based embeddings
block to deal with different difficulty levels information and an LSTM block to
process the sequential characteristic in students' actions. LBKT achieves the
best performance on most benchmark datasets on the metrics of ACC and AUC.
Additionally, an ablation study is conducted to analyse the impact of each
component of LBKT's overall performance. Moreover, we used t-SNE as the
visualisation tool to demonstrate the model's embedding strategy. The results
indicate that LBKT is faster, more interpretable, and has a lower memory cost
than the traditional deep learning based Knowledge Tracing methods.
