  Knowledge tracing (KT) is a field of study that predicts the future
performance of students based on prior performance datasets collected from
educational applications such as intelligent tutoring systems, learning
management systems, and online courses. Some previous studies on KT have
concentrated only on the interpretability of the model, whereas others have
focused on enhancing the performance. Models that consider both
interpretability and the performance improvement have been insufficient.
Moreover, models that focus on performance improvements have not shown an
overwhelming performance compared with existing models. In this study, we
propose MonaCoBERT, which achieves the best performance on most benchmark
datasets and has significant interpretability. MonaCoBERT uses a BERT-based
architecture with monotonic convolutional multihead attention, which reflects
forgetting behavior of the students and increases the representation power of
the model. We can also increase the performance and interpretability using a
classical test-theory-based (CTT-based) embedding strategy that considers the
difficulty of the question. To determine why MonaCoBERT achieved the best
performance and interpret the results quantitatively, we conducted ablation
studies and additional analyses using Grad-CAM, UMAP, and various visualization
techniques. The analysis results demonstrate that both attention components
complement one another and that CTT-based embedding represents information on
both global and local difficulties. We also demonstrate that our model
represents the relationship between concepts.
