Predicting Dropout Rates of MOOCs with Sliding Window Model
Lu Xiaohang1, Wang Shengqing2(), Huang Junjie1, Chen Wenguang1, Yan Zengwang1
1Department of Information Management, Peking University, Beijing 100871, China 2Center of Faculty Development, Peking University, Beijing 100871, China
[Objective] This paper aims to improve the MOOCs curriculum quality and pedagogy by analyzing the dropout behaviors with data from the MOOC of Peking University on Coursera. [Methods] We extracted 19 major features from the logs and then constructed a siding window model to predict the dropout rates. [Results] The precision of the proposed model was maintained above 90%. The SVM and LSTM methods further improved the performance of the proposed model. [Limitations] The new method needs to be examined with smaller sized courses. [Conclusions] Predicting dropout rates could help us improve the course quality effectively.
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