Deep Learning for Dropout Prediction in MOOCs

被引：27

作者：

Sun, Di ^{[1
]}

Mao, Yueheng ^{[2
]}

Du, Junlei ^{[3
]}

Xu, Pengfei ^{[2
]}

Zheng, Qinhua ^{[4
]}

Sun, Hongtao ^{[5
]}

机构：

[1] Syracuse Univ, IDD&E, Syracuse, NY USA

[2] Beijing Normal Univ, Sch Artificial Intelligence, Beijing, Peoples R China

[3] Beijing Normal Univ, Res Ctr Distance Educ, Beijing, Peoples R China

[4] Beijing Normal Univ, Fac Educ, Beijing, Peoples R China

[5] Beijing Normal Univ, Ctr Info & Network Technol, Beijing, Peoples R China

来源：

2019 EIGHTH INTERNATIONAL CONFERENCE ON EDUCATIONAL INNOVATION THROUGH TECHNOLOGY (EITT) | 2019年

关键词：

MOOCs; Dropout Prediction; completion; RNN;

D O I：

10.1109/EITT.2019.00025

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In recent years, the rapid rise of massive open online courses (MOOCs) has aroused great attention. Dropout prediction or identifying students at risk of dropping out of a course is an open problem for MOOC researchers and providers. This paper formulates the dropout prediction problem as predicting how much content in the whole course syllabus can be completed by the student. A dropout rate prediction model is based on a recurrent neural network (RNN), and an URL embedding layer is proposed to solve this problem. The results show that the prediction accuracy of the model is significantly higher than that of the traditional machine learning model.

引用

页码：87 / 90

页数：4

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