Who will be the next to drop out? Anticipating dropouts in MOOCs with multi-view features

Jiang, F; Li, W

Who will be the next to drop out? Anticipating dropouts in MOOCs with multi-view features

Jiang, F Li, W

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Publication Type:: Journal Article
Citation:: International Journal of Performability Engineering, 2017, 13 (2), pp. 201 - 210
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Jiang, F	en_US
dc.contributor.author	Li, W https://orcid.org/0000-0003-4941-8814	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	International Journal of Performability Engineering, 2017, 13 (2), pp. 201 - 210	en_US
dc.identifier.issn	0973-1318	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124024
dc.description.abstract	© Totem Publisher, Inc. Massive Open Online Courses (MOOCs) have gained rising popularity in recent years. However, MOOCs have faced a challenge of a large number of students dropping out from courses. Most studies predict dropouts based on some general features extracted from historical learning behavior and ignore the diversity of the behaviors. To solve this problem, we first analyze each type of learning behavior independently to get the different behavior patterns between dropout and retention students. We then derive multiple kinds of features from the corresponding types of learning behavior records. After that, we propose three algorithms that make use of these features. The first one trains several detectors based on each types of features. The second utilizes multi-view ensemble learning to anticipate dropouts. The third applies semi-supervised co-training to train the detector. Experimental results justify the rationality of the multi-view features and the proposed approaches achieve better prediction performances.	en_US
dc.relation.ispartof	International Journal of Performability Engineering	en_US
dc.title	Who will be the next to drop out? Anticipating dropouts in MOOCs with multi-view features	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	13	en_US
utslib.for	0999 Other Engineering	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

© Totem Publisher, Inc. Massive Open Online Courses (MOOCs) have gained rising popularity in recent years. However, MOOCs have faced a challenge of a large number of students dropping out from courses. Most studies predict dropouts based on some general features extracted from historical learning behavior and ignore the diversity of the behaviors. To solve this problem, we first analyze each type of learning behavior independently to get the different behavior patterns between dropout and retention students. We then derive multiple kinds of features from the corresponding types of learning behavior records. After that, we propose three algorithms that make use of these features. The first one trains several detectors based on each types of features. The second utilizes multi-view ensemble learning to anticipate dropouts. The third applies semi-supervised co-training to train the detector. Experimental results justify the rationality of the multi-view features and the proposed approaches achieve better prediction performances.

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http://hdl.handle.net/10453/124024