In view of the lack of interpretability and data dependence of RNN-based deep knowledge tracking model, a deep knowledge tracing model integrating attention mechanism is proposed. First, learn the embedded representation of students' historical interaction, and then learn specific weights based on the topic's attention mechanism to identify and strengthen the impact of students' historical interaction on the knowledge state at each moment to different degrees; comparative experiments will be carried out on two data sets, and the best performance will be achieved in the ASSISTments2012 dataset, and the problem of long sequence dependence will be alleviated to some extent. This model can capture students' knowledge state more accurately and predict students' future performance more efficiently.
Bayesian knowledge tracking model is used to track learners' knowledge state and predict their mastery level and future performance in intelligent teaching system. The original BKT model assumes that learners do not forget any knowledge after learning. This assumption will lead to the deviation between the predicted results of the model and the actual situation. In order to deal with above situations, this paper proposes a Bayesian knowledge tracking model based on learner behavior and forgetting factors. By using the decision tree algorithm to obtain the behavior node data information, and then initialize the forgetting parameters and assign values to update the algorithm of learners' knowledge mastery level.
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