Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/220
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dc.contributor.authorCrossley, Scott
dc.contributor.authorDascalu, Mihai
dc.contributor.authorMcNamara, Danielle S.
dc.contributor.authorBaker, Ryan
dc.contributor.authorTrausan-Matu, Stefan
dc.date.accessioned2017-06-19T10:50:27Z
dc.date.accessioned2017-06-19T08:57:59Z-
dc.date.available2017-06-19T10:50:27Z
dc.date.available2017-06-19T08:57:59Z-
dc.date.issued2017-07
dc.identifier.citationCrossley, 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.en_US
dc.identifier.urihttps:dx.doi.org/10.22318/cscl2017.17
dc.identifier.urihttps://repository.isls.org/handle/1/220-
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisherPhiladelphia, PA: International Society of the Learning Sciences.en_US
dc.titlePredicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysisen_US
dc.typeBook chapteren_US
Appears in Collections:CSCL 2017

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