Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics

Mohammad Khalil, Christian Kastl, Martin Ebner

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Massive Open Online Courses are remote courses that excel in their students’ heterogeneity and quantity. Due to the peculiarity of being massiveness, the large datasets generated by MOOCs platforms require advance tools to reveal hidden patterns for enhancing learning and educational environments. This paper offers an interesting study on using one of these tools, clustering, to portray learners’ engagement in MOOCs. The research study analyse a university mandatory MOOC, and also opened to the public, in order to classify students into appropriate profiles based on their engagement. We compared the clustering results across MOOC variables and finally, we evaluated our results with an eighties students’ motivation scheme to examine the contrast between classical classes and MOOCs classes. Our research pointed out that MOOC participants are strongly following the Cryer’s scheme of ELTON (1996).
Original languageEnglish
Title of host publicationProceedings of the European Stakeholder Summit on experiences and best practices in and around MOOCs
Place of PublicationNorderstedt
Publication statusPublished - 2016
EventEMOOCs 2016 - European Stakeholders Summit - Graz, Austria
Duration: 22 Feb 201624 Feb 2016


ConferenceEMOOCs 2016 - European Stakeholders Summit

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Theoretical


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