Disciplinary differences in blended learning design: a network analytic study

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

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

5 Citations (Scopus)


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
Subtitle of host publicationProceedings of the Tenth International Conference on Learning Analytics & Knowledge
EditorsMaren Scheffel, Vitomir Kovanović, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450377126
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th
https://lak20.solaresearch.org (Website)
https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3375462 (Website)


ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
Internet address


  • Epistemic network analysis
  • Faculty
  • Learning activity types

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