Abstract: Knowledge tracing is the core and key to build an adaptive education system,and it is often used to capture students' knowledge states and predict their future performance.Previous knowledge tracing models only model questions and skills based on structural information,unable to utilize the multimodal information of questions and skills to construct their interdependence.Additionally,the memory level of students is only quantified by time,without considering the influence of different modalities.Therefore,a multimodality and forgetting mechanisms model for knowledge tracing(MFKT)is proposed.Firstly,for question and skill nodes,a image-text matching task is used to optimize the unimodal embedding,and obtain the association weight calculation of questions and skills by calculating the similarity between nodes after multimodal fusion to generate the embedding of question nodes.Secondly,the student's knowledge state is obtained through the long short-term memory network,and forgetting factors are incorporated into their response records to generate student embeddings.Finally,the correlation strength between students and questions is calculated based on the student's response frequency and the effective memory rate of different modalities.Information propagation is performed using a graph attention network to predict the student's response to different questions.Comparative experiments and ablation experiments on two real classroom self-collected datasets show that our method has better prediction accuracy compared to other graph-based knowledge tracing models,and the design of multimodality and forgetting mechanisms effectively improves the prediction performance of the original model.At the same time,through the visual analysis of a specific case,further illustrate the practical application effect of this method.

Key words: Knowledge tracing, Multimodality, Heterogeneous graph, Forgetting mechanism