Counting clicks is not enough: Validating a theorized model of engagement in learning analytics

Ed Fincham, Srećko Joksimović, Alexander, Jan Paul Van Staalduinen, Vitomir Kovanović, Dragan Gašević

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    10 Citations (Scopus)

    Abstract

    Student engagement is often considered an overarching construct in educational research and practice. Though frequently employed in the learning analytics literature, engagement has been subjected to a variety of interpretations and there is little consensus regarding the very definition of the construct. This raises grave concerns with regards to construct validity: namely, do these varied metrics measure the same thing? To address such concerns, this paper proposes, quantifies, and validates a model of engagement which is both grounded in the theoretical literature and described by common metrics drawn from the field of learning analytics. To identify a latent variable structure in our data we used exploratory factor analysis and validated the derived model on a separate sub-sample of our data using confirmatory factor analysis. To analyze the associations between our latent variables and student outcomes, a structural equation model was fitted, and the validity of this model across different course settings was assessed using MIMIC modeling. Across different domains, the broad consistency of our model with the theoretical literature suggest a mechanism that may be used to inform both interventions and course design.

    Original languageEnglish
    Title of host publicationProceedings of the 9th International Conference on Learning Analytics and Knowledge
    Subtitle of host publicationLearning Analytics to Promote Inclusion and Success, LAK 2019
    EditorsSharon Hsiao, Jim Cunningham
    Place of PublicationNew York, NY, USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages501-510
    Number of pages10
    ISBN (Electronic)978-1-4503-6256-6
    DOIs
    Publication statusPublished - 2019
    Event9th International Conference on Learning Analytics and Knowledge, LAK 2019 - Tempe, United States
    Duration: 4 Mar 20198 Mar 2019

    Conference

    Conference9th International Conference on Learning Analytics and Knowledge, LAK 2019
    CountryUnited States
    CityTempe
    Period4/03/198/03/19

    Keywords

    • Engagement
    • Factor analysis
    • Measurement invariance
    • MOOCs
    • Structural equation modeling

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