Abstract
Based on the datasets from the MOOCs of Peking University running on the Coursera platform, we extract 19 major features of tune in after analyzing the log structure. To begin with, we focus on the characteristics of start and dropout point of learners through the statistics of their start time and dropout time. Then we construct two models. First, several approaches of machine learning are used to build a sliding window model for predicting the dropout probabilities in a certain course. Second, SVM is used to build the model for predicting whether a student can get a score at the end of the course. For instructors and designers of MOOCs, dynamically tracking the records of the dropouts could be helpful to improve the course quality in order to reduce the dropout rate.
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Acknowledgments
This paper is supported by Peking University Education Foundation (2015ZD05) and National Natural Science Foundation of China (61472013).
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Lu, X., Wang, S., Huang, J., Chen, W., Yan, Z. (2017). What Decides the Dropout in MOOCs?. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_25
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DOI: https://doi.org/10.1007/978-3-319-55705-2_25
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