Assessing learning in MOOCs through interactions between learners

F.M.R. Brouns*, O. Firssova

*Corresponding author for this work

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

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    This paper presents a retrospective analysis of learning in a MOOC as recon-structed from the conversations that learners conducted in MOOC group forums while performing the course tasks. A mixed method approach was applied to ana-lyse the quantity and the quality of these conversations. Two activity patterns were distinguished – in groups with higher activity levels, there were more indi-vidual contributions (posts) on more course themes and these contributions were broader spread throughout the course. In high activity groups there was also more interaction between participants, i.e., more questions, answers, explanations and elaborations. The presented study demonstrates how modeling interactions in group forums helps to elicit individual and emerging group knowledge construc-tion and thus supports defining MOOC learning, informs MOOC design and provides insights on how assessing MOOC learning can be automated
    Original languageEnglish
    Title of host publicationTechnology Enhanced Assessment
    Subtitle of host publication21st International Conference, TEA 2018, Amsterdam, The Netherlands, December 10–11, 2018, Revised Selected Papers
    EditorsSilvester Draaijer , Desirée Joosten-ten Brinke , Eric Ras
    PublisherSpringer International Publishing AG
    Number of pages13
    ISBN (Electronic)978-3-030-25264-9
    ISBN (Print)978-3-030-25263-2
    Publication statusPublished - 13 Jul 2019
    EventTEA 2018 Technology Enhanced Assessment Conference - Amsterdam, Netherlands
    Duration: 10 Dec 201811 Dec 2018

    Publication series

    SeriesCommunications in Computer and Information Science


    ConferenceTEA 2018 Technology Enhanced Assessment Conference


    • MOOC learning
    • assessing learning
    • knowledge building
    • learner interactions
    • mixed methods
    • text analysis


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