Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics

Mohammad Khalil, Christian Kastl, Martin Ebner

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

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).
Originalspracheenglisch
TitelProceedings of the European Stakeholder Summit on experiences and best practices in and around MOOCs
ErscheinungsortNorderstedt
Herausgeber (Verlag)BoD
Seiten265-278
PublikationsstatusVeröffentlicht - 2016
VeranstaltungEMOOCs 2016 - European Stakeholders Summit - Graz, Österreich
Dauer: 22 Feb. 201624 Feb. 2016

Konferenz

KonferenzEMOOCs 2016 - European Stakeholders Summit
Land/GebietÖsterreich
OrtGraz
Zeitraum22/02/1624/02/16

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Theoretical

Fingerprint

Untersuchen Sie die Forschungsthemen von „Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren