An Eye-tracking Study of Notational, Informational, and Emotional Aspects of Learning Analytics Representations

Ravi Vatrapu, Peter Reimann, Susan Bull , Matthew Johnson

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


    This paper presents an eye-tracking study of notational, informational, and emotional aspects of nine different notational systems (Skill Meters, Smilies, Traffic Lights, Topic Boxes, Collective Histograms, Word Clouds, Textual Descriptors, Table, and Matrix) and three different information states (Weak, Average, & Strong) used to represent student's learning. Findings from the eye-tracking study show that higher emotional activation was observed for the metaphorical notations of traffic lights and smilies and collective representations. Mean view time was higher for representations of the "average" informational learning state. Qualitative data analysis of the think-aloud comments and post-study interview show that student participants reflected on the meaning-making opportunities and action-taking possibilities afforded by the representations. Implications for the design and evaluation of learning analytics representations and discourse environments are discussed.
    Original languageEnglish
    Title of host publicationProceedings of the Third International Conference on Learning Analytics and Knowledge. LAK '13
    EditorsDan Suthers, Katrien Verbert, Erik Duval , Xavier Ochoa
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery
    Publication date2013
    ISBN (Print)9781450317856
    Publication statusPublished - 2013
    EventThe 3rd International Conference on Learning Analytics and Knowledge - University of Leuven, Leuven, Belgium
    Duration: 8 Apr 201312 Apr 2013
    Conference number: 3


    ConferenceThe 3rd International Conference on Learning Analytics and Knowledge
    LocationUniversity of Leuven
    Internet address
    SeriesACM International Conference Proceeding Series

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