Abstract
With the deep integration of information technology and education, Massive Open Online Courses (MOOCs) become popular and receive high attention. Although MOOCs are popular among people, it faces a great challenge—the high dropout rate, which affects its development. Predicting the dropout rate in advance can take relevant measures to avoid as many dropouts as possible. Traditional machine learning classification prediction and single sequence label prediction methods are difficult to accurately predict complex user behaviors. To solve the problem, in this paper, we perform a deep analysis of user learning behavior to find that user activity shows a periodic distribution based on the time of course release. In addition, user gender and course category also affect users’ behaviors. To this end, we propose a deep model based on recurrent network which combines the influence factors of cyclical historical behavior on the basis of a single sequence of events. Meanwhile, we combine behavior periodicity with attention mechanism to select effective historical behavior impact factors. Then we embed the attributes of user and course to predict the dropout rate. Finally, experiments on different data sets show that our approach performs better than the state-of-the art methods.
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Acknowledgements
This work is supported by the key projects of the national natural science foundation of China (No. U1811263), the major technical innovation project of Hubei Province (No. 2019AAA072), the National Natural Science Foundation of China (No. 61572378), the Teaching Research Project of Wuhan University (No. 2018JG052), the Natural Science Foundation of Hubei Province (No. 2017CFB420). We also thank anonymous reviewers for their helpful reports.
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Wu, F., Zhang, J., Shi, Y., Yang, X., Song, W., Peng, Z. (2020). Predicting MOOCs Dropout with a Deep Model. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12343. Springer, Cham. https://doi.org/10.1007/978-3-030-62008-0_34
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DOI: https://doi.org/10.1007/978-3-030-62008-0_34
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