Abstract
In recent years, online education is transforming from mobile education to intelligent education, and the rapid development of machine learning breathe into intelligent education with powerful energy. Deep Knowledge Tracing (DKT) is a state of the art method for modeling students’ abilities which is changing by time. It can accurately predict students’ mastery of knowledge or skill as well as their future performance. In this paper, we study the structure of the DKT model and proposed a new deep knowledge tracing model based on Bidirectional Recurrent Neural Network (BiRNN-DKT). We have also optimized the incorporating of data preprocessing and external features to improve model performance. Experiments show that the model can not only predict students’ performance by capturing their history performance, but also get more accurate learning status simulation by integrating past and future context sequence information into the model multiple knowledge concepts. Compared with the traditional model, the proposed BiRNN-DKT gets an improvement in predicting students’ knowledge ability and performance, and has great advantages in measuring the level of knowledge acquired by students.
This work was supported by the National Natural Science Foundation of China under Grant no. U1811261, and the National Natural Science Foundation of China under Grant no.51607029.
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Xu, B., Yan, S., Yang, D. (2019). BiRNN-DKT: Transfer Bi-directional LSTM RNN for Knowledge Tracing. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_3
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DOI: https://doi.org/10.1007/978-3-030-30952-7_3
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