An eye-tracking study of notational, informational, and emotional aspects of learning analytics representations

R. Vatrapu, P. Reimann, S. Bull, M. Johnson

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

9 Citations (Scopus)

Abstract

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 publicationACM International Conference Proceeding Series: Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages125-134
Number of pages10
ISBN (Print)9781450317856
DOIs
Publication statusPublished - 1 Jan 2013

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