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| 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 |
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Abstract [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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Received: 27 February 2017
Published: 24 May 2017
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