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Please use this identifier to cite or link to this item: http://hdl.handle.net/1820/9653
Title: Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis
Authors: Crossley, Scott
Dascalu, Mihai
McNamara, Danielle S.
Baker, Ryan
Trausan-Matu, Stefan
Keywords: Cohesion Network Analysis
Massive Open Online Courses
prediction of completion rates
longitudinal analysis
ReaderBench framework
Issue Date: 2017
Publisher: ISLS
Citation: Crossley, S. A., Dascalu, M., Baker, M., McNamara, D. S., & Trausan-Matu, S. (2017). Predicting Success in Massive Open Online Courses (MOOC) Using Cohesion Network Analysis. In 12th Int. Conf. on Computer-Supported Collaborative Learning (CSCL 2017) (pp. 103–110). Philadelphia, PA: ISLS.
Abstract: This study uses Cohesion Network Analysis (CNA) indices to identify student patterns related to course completion in a massive open online course (MOOC). This analysis examines 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 CNA indices predict with substantial accuracy (76%) whether students complete the MOOC, helping us to better understand student retention in this MOOC and to develop more actionable automated signals of student success.
URI: http://hdl.handle.net/1820/9653
Appears in Collections:1. RAGE Publications

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