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Map-based Visual Analytics of Moving Learners

Author

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  • Christian Sailer

    (Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland)

  • Peter Kiefer

    (Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland)

  • Joram Schito

    (Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland)

  • Martin Raubal

    (Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland)

Abstract

Location-based mobile learning (LBML) is a type of mobile learning in which the learning content is related to the location of the learner. The evaluation of LBML concepts and technologies is typically performed using methods known from classical usability engineering, such as questionnaires or interviews. In this paper, the authors argue for applying visual analytics to spatial and spatio-temporal visualizations of learners' trajectories for evaluating LBML. Visual analytics supports the detection and interpretation of spatio-temporal patterns and irregularities in both, single learners' as well as multiple learners' trajectories, thus revealing learners' typical behavior patterns and potential problems with the LBML software, hardware, the didactical concept, or the spatial and temporal embedding of the content.

Suggested Citation

  • Christian Sailer & Peter Kiefer & Joram Schito & Martin Raubal, 2016. "Map-based Visual Analytics of Moving Learners," International Journal of Mobile Human Computer Interaction (IJMHCI), IGI Global, vol. 8(4), pages 1-28, October.
    • Handle: RePEc:igg:jmhci0:v:8:y:2016:i:4:p:1-28
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