Exploring online course sociograms using cohesion network analysis

Maria Dorinela Sirbu, Mihai Dascalu, Scott A. Crossley, Danielle McNamara, Tiffany Barnes, Collin F. Lynch, Stefan Trausan-Matu

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

7 Scopus citations


Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in on-line courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and to identify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
EditorsRose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, Roberto Martinez-Maldonado, H. Ulrich Hoppe
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319938455
StatePublished - 2018
Event19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom
Duration: Jun 27 2018Jun 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10948 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other19th International Conference on Artificial Intelligence in Education, AIED 2018
Country/TerritoryUnited Kingdom


  • Cohesion network analysis
  • Interaction patterns
  • Online courses
  • Participants clustering
  • Sociograms

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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