Most of the existing knowledge tracking methods ignore the dynamic influence of psychological states on the learning process, resulting in limited accuracy of personalised prediction.To this end, this paper proposes a psychomotivation-driven personalised knowledge tracking graph neural network.By integrating motivational factors such as concentration and curiosity and constructing a student-knowledge heterogeneous graph, it can simulate the learning process more accurately.Experiments on the assistments2012 and ednet public datasets show that psychomotivation-driven personalised knowledge tracking graph neural network has an average improvement of 1.2% in the area under the roc curve metric and 2.1% in the prediction accuracy compared to the optimal baseline model, and the improvement is statistically significant.This study provides an effective approach for achieving fine-grained learning state assessment that integrates cognition and emotion.
Wang et al. (Thu,) studied this question.