• University:Computational Social Science Laboratory (CSSL), ITM, Copenhagen Business School, Norwegian School of Information Technology (NITH), MTO Psychologische Forschung und Beratung, Electronic, Electrical and Computer Engineering, University of Birmingham
  • Authors:Ravi Vatrapu, Peter Reimann, Susan Bull, and Matthew Johnson


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, World 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 smiles and collective 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.

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