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Gaze-Based Attention-Aware Cyberlearning Technologies

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Mind, Brain and Technology

Abstract

Over a century of cognitive psychology has taught us that attention plays a central role in cognition, especially in learning. Accordingly, the central thesis of this chapter is that next-generation learning technologies should include mechanisms to model and respond to learners’ attentional states. As a step in this direction, this chapter proposes a macro-theoretic framework that encompasses various forms of overt and covert states of attention (e.g., alternative vs. divided attention) and inattention (e.g., zone outs vs. tune outs). It then provides examples of three attention-aware cyberlearning technologies that utilize eye tracking as a window into learners’ attentional states. The first of these is GazeTutor, which uses eye movements to detect overt inattentional lapses and attempts to redirect attention with a set of gaze-reactive dialogue moves. The second system address more covert forms of inattention by using eye movements to detect instances of mind wandering and responding with interpolated questions, self-explanations, and re-reading opportunities. The third example attempts to graduate such technologies from the lab into real-world classrooms by using consumer-off-the-shelf eye trackers as entire classes of students individually interact with a cyberlearning technology. The chapter concludes by suggesting key next-steps for the field of attentional-aware cyberlearning.

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Acknowledgements

This research was supported by the National Science Foundation (NSF) (DRL 1235958 and IIS 1523091). Any opinions, findings, and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.

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  1. Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA

    Sidney K. D’Mello

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  1. College of Information, University of North Texas Computational Neuropsychology and Simulation, Denton, TX, USA

    Thomas D. Parsons

  2. Department of Learning Technologies, University of North Texas, Denton, TX, USA

    Lin Lin

  3. Department of Learning Technologies, Fort Worth Museum of Science and History, University of North Texas, Fort Worth, TX, USA

    Deborah Cockerham

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D’Mello, S.K. (2019). Gaze-Based Attention-Aware Cyberlearning Technologies. In: Parsons, T.D., Lin, L., Cockerham, D. (eds) Mind, Brain and Technology. Educational Communications and Technology: Issues and Innovations. Springer, Cham. https://doi.org/10.1007/978-3-030-02631-8_6

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