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Combining click-stream data with NLP tools to better understand MOOC completion

Combining click-stream data with NLP tools to better understand MOOC completion Combining Click-Stream Data with NLP Tools to Better Understand MOOC Completion Scott Crossley Georgia State University 25 Park Place, Ste 1500 Atlanta, GA 30303 Luc Paquette University of Illinois at UrbanaChampaign 1310 S. 6th St. Champaign, IL, 61820 Mihai Dascalu University Politehnica of Bucharest 313 SplaiulIndepententei Bucharest, Romania scrossley@gsu.edu mihai.dascalu@cs.pub.ro lpaq@illinois.edu Danielle S. McNamara Arizona State University PO Box 872111 Tempe, AZ 85287 Ryan S. Baker Teachers College, Columbia University 525 West 120th Street New York, NY, 10027 baker2@exchange.tc.columbia.edu dsmcnamara1@gmail.com ABSTRACT Keywords MOOC, click-stream data, educational data mining, natural language processing, sentiment analysis, educational success, predictive analytics Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Combining click-stream data with NLP tools to better understand MOOC completion

Association for Computing Machinery — Apr 25, 2016

Combining click-stream data with NLP tools to better understand MOOC completion


Combining Click-Stream Data with NLP Tools to Better Understand MOOC Completion Scott Crossley Georgia State University 25 Park Place, Ste 1500 Atlanta, GA 30303 Luc Paquette University of Illinois at UrbanaChampaign 1310 S. 6th St. Champaign, IL, 61820 Mihai Dascalu University Politehnica of Bucharest 313 SplaiulIndepententei Bucharest, Romania scrossley@gsu.edu mihai.dascalu@cs.pub.ro lpaq@illinois.edu Danielle S. McNamara Arizona State University PO Box 872111 Tempe, AZ 85287 Ryan S. Baker Teachers College, Columbia University 525 West 120th Street New York, NY, 10027 baker2@exchange.tc.columbia.edu dsmcnamara1@gmail.com ABSTRACT Keywords MOOC, click-stream data, educational data mining, natural language processing, sentiment analysis, educational success, predictive analytics Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of clickstream data and NLP indices can predict with substantial accuracy (78%) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to...
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References (45)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISBN
978-1-4503-4190-5
doi
10.1145/2883851.2883931
Publisher site
See Article on Publisher Site

Abstract

Combining Click-Stream Data with NLP Tools to Better Understand MOOC Completion Scott Crossley Georgia State University 25 Park Place, Ste 1500 Atlanta, GA 30303 Luc Paquette University of Illinois at UrbanaChampaign 1310 S. 6th St. Champaign, IL, 61820 Mihai Dascalu University Politehnica of Bucharest 313 SplaiulIndepententei Bucharest, Romania scrossley@gsu.edu mihai.dascalu@cs.pub.ro lpaq@illinois.edu Danielle S. McNamara Arizona State University PO Box 872111 Tempe, AZ 85287 Ryan S. Baker Teachers College, Columbia University 525 West 120th Street New York, NY, 10027 baker2@exchange.tc.columbia.edu dsmcnamara1@gmail.com ABSTRACT Keywords MOOC, click-stream data, educational data mining, natural language processing, sentiment analysis, educational success, predictive analytics Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis

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