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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (4): 67-75    DOI: 10.11925/infotech.2096-3467.2017.04.08
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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.

Key wordsMOOC      Dropout Point      Dropout Rates      Sliding Window Model      Dropout Prediction     
Received: 27 February 2017      Published: 24 May 2017
ZTFLH:  G434  

Cite this article:

Lu Xiaohang,Wang Shengqing,Huang Junjie,Chen Wenguang,Yan Zengwang. Predicting Dropout Rates of MOOCs with Sliding Window Model. Data Analysis and Knowledge Discovery, 2017, 1(4): 67-75.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.04.08     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I4/67

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pkubioinfo-001 2013生物信息学001 18 367 18 367 1 620 520 2.8312
pkubioinfo-003 生物信息学-导论与方法 16 958 16 072 909 360 2.1229
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[1] Jiang Wu,Chaocheng He,Panhao Ma. Analyzing Interaction of MOOC Users with Iteration Super Centrality[J]. 数据分析与知识发现, 2017, 1(8): 1-8.
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