Long sequence temporal knowledge tracing for student performance prediction via integrating LSTM and informer

Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies. In this study, we propose a long-sequence time-series forecasting pipeline for knowledge tracing that leverages both time stamp and exercise sequences. Firstly, we introduce a bidirectional LSTM model to tackle the embeddings of exercise-answering records. Secondly, we incorporate both the students' exercising recordings and the time stamps into a vector for each record. Next, a sequence of vectors is taken as input for the proposed Informer model, which utilizes the probability-sparse self-attention mechanism. Note that the probability sparse self-attention module can address the quadratic computational complexity issue of the canonical encoder-decoder architecture. Finally, we integrate temporal information and individual knowledge states to implement the answers to a sequence of target exercises. To evaluate the performance of the proposed LSTKT model, we conducted comparison experiments with state-of-the-art knowledge tracing algorithms on a publicly available dataset. This model demonstrates quantitative improvements over existing models. In the Assistments2009 dataset, it achieved an accuracy of 78.49% and an AUC of 78.81%. For the Assistments2017 dataset, it reached an accuracy of 74.22% and an AUC of 72.82%. In the EdNet dataset, it attained an accuracy of 68.17% and an AUC of 70.78%.