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

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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.

Publication information

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
StatePublished - 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
SeriesACM International Conference Proceeding Series

ID: 38382430