Predicting success in massive open online courses (Moocs) using cohesion network analysis

Scott A. Crossley, Mihai Dascalu, Danielle S. McNamara, Ryan Baker, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMaking a Difference
Subtitle of host publicationPrioritizing Equity and Access in CSCL - 12th International Conference on Computer Supported Collaborative Learning, CSCL 2017 - Conference Proceedings
EditorsBrian K. Smith, Marcela Borge, Emma Mercier, Kyu Yon Lim
PublisherInternational Society of the Learning Sciences (ISLS)
Pages103-110
Number of pages8
ISBN (Electronic)9780990355007
StatePublished - 2017
Event12th International Conference on Computer Supported Collaborative Learning - Making a Difference: Prioritizing Equity and Access in CSCL, CSCL 2017 - Philadelphia, United States
Duration: Jun 18 2017Jun 22 2017

Publication series

NameComputer-Supported Collaborative Learning Conference, CSCL
Volume1
ISSN (Print)1573-4552

Conference

Conference12th International Conference on Computer Supported Collaborative Learning - Making a Difference: Prioritizing Equity and Access in CSCL, CSCL 2017
Country/TerritoryUnited States
CityPhiladelphia
Period6/18/176/22/17

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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