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  4. Vol 9, Issue 1, 2024

Graph Convolutional Neural Network Knowledge Tracking Based on Response Time Feature

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DOI: 10.23977/jeis.2024.090102 | Downloads: 34 | Views: 1091

Author(s)

Juwei Dao 1, Li Hong 1

Affiliation(s)

1 College of Information, Yunnan Normal University, Kunming, China

Corresponding Author

Juwei Dao

ABSTRACT

The GCKT model is proposed based on the following two feature optimization ideas. Firstly, Graph Convolutional Neural Network (GCN) is applied to knowledge tracking in order to enhance local features, improve the effect of the model, and reduce the risk of overfitting. In addition, in order to solve the problem that the current GKT model only depends on the relevant content of the learner's answer and input few features, which leads to low prediction accuracy, the model in this paper uses the time features obtained by incorporating the learner's answer time of each exercise, and gives the learner each answer record as the model input.To improve the accuracy of prediction. Finally, the effectiveness and rationality of the proposed method are proved by experiments.

KEYWORDS

Knowledge Tracking, Deep Learning, Graph Convolutional Neural Network

CITE THIS PAPER

Juwei Dao, Li Hong, Graph Convolutional Neural Network Knowledge Tracking Based on Response Time Feature. Journal of Electronics and Information Science (2024) Vol. 9: 7-12. DOI: http://dx.doi.org/10.23977/10.23977/jeis.2024.090102.

REFERENCES

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