Abstract
High dropout rates and unsatisfactory learning outcomes have become the main problems of MOOC platforms, and the intervention of dropout prediction at the early stage is an effective way to solve these problems. To this end, we propose a dropout prediction model based on Bayesian networks (Dropout Prediction Bayesian Network, DPBN), which uses mutual information and the pruning to construct the structure of DPBN, and then the parameters are learned by the maximum likelihood estimation (MLE). The model can represent the influence of each feature on the dropout rate and enhance the interpretability of the model. Based on the constructed DPBN, we adopt the exact inference method to predict the dropouts successfully. The experimental results demonstrate the accuracy and validity of our proposed method.
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Acknowledgment
This work is supported by School of Information Science and Technology, Yunnan Normal University Graduate Research Innovation Fund (NO. CIC2022011),Scientific research foundation of Yunnan Provincial Department of Education (Grant No. 2022Y180), Yunnan Innovation Team of Education Informatization for Nationalities, and Scientific Technology Innovation Team of Educational Big Data Application Technology in University of Yunnan Province.
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Shi, S., Zhang, S., Hao, J., Chen, K., Wang, J. (2023). MOOC Dropout Prediction Based on Bayesian Network. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_40
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DOI: https://doi.org/10.1007/978-3-031-20102-8_40
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