Improvement of Bayesian knowledge tracking behavior model

Pei Pei

doi:10.1117/12.2640365

1 August 2022 Improvement of Bayesian knowledge tracking behavior model

Pei Pei

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122570Z (2022) https://doi.org/10.1117/12.2640365
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

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.

Citation Download Citation

Pei Pei "Improvement of Bayesian knowledge tracking behavior model", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122570Z (1 August 2022); https://doi.org/10.1117/12.2640365

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