Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/220
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
Issue Date: Jul-2017
Publisher: Philadelphia, PA: International Society of the Learning Sciences.
Citation: Crossley, S., Dascalu, M., McNamara, D. S., Baker, R., & Trausan-Matu, S. (2017). Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis In Smith, B. K., Borge, M., Mercier, E., and Lim, K. Y. (Eds.). (2017). Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning (CSCL) 2017, Volume 1. Philadelphia, PA: International Society of the Learning Sciences.
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: https:dx.doi.org/10.22318/cscl2017.17
https://repository.isls.org/handle/1/220
Appears in Collections:CSCL 2017

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