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DC Field | Value | Language |
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dc.contributor.author | Crossley, Scott | |
dc.contributor.author | Dascalu, Mihai | |
dc.contributor.author | McNamara, Danielle S. | |
dc.contributor.author | Baker, Ryan | |
dc.contributor.author | Trausan-Matu, Stefan | |
dc.date.accessioned | 2017-06-19T10:50:27Z | |
dc.date.accessioned | 2017-06-19T08:57:59Z | - |
dc.date.available | 2017-06-19T10:50:27Z | |
dc.date.available | 2017-06-19T08:57:59Z | - |
dc.date.issued | 2017-07 | |
dc.identifier.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. | en_US |
dc.identifier.uri | https:dx.doi.org/10.22318/cscl2017.17 | |
dc.identifier.uri | https://repository.isls.org/handle/1/220 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Philadelphia, PA: International Society of the Learning Sciences. | en_US |
dc.title | Predicting Success in Massive Open Online Courses (MOOCs) Using Cohesion Network Analysis | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | CSCL 2017 |
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