Disciplinary Differences in Blended Learning Design: A Network Analytic Study

Alexander Whitelock-Wainwright, Yi-Shan Tsai, Kayley Lyons, Svetlana Khalif, Mike Bryant, Kris Ryan, Dragan Gasevic

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

Abstract

Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016-2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.
Original languageEnglish
Title of host publicationLAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
PublisherAssociation for Computing Machinery (ACM)
Pages579-588
Number of pages10
ISBN (Print)9781450377126
DOIs
Publication statusPublished - 23 Mar 2020
EventThe 10th International Learning Analytics & Knowledge Conference - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10
https://lak20.solaresearch.org/

Conference

ConferenceThe 10th International Learning Analytics & Knowledge Conference
Abbreviated titleLAK20
CountryGermany
CityFrankfurt
Period23/03/2027/03/20
Internet address

Keywords

  • Faculty
  • epistemic network analysis
  • learning activity types

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