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The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor

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Artificial Intelligence in Education (AIED 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10331))

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Abstract

In this paper, we investigate the relationship between students’ (N = 28) individual differences and visual attention to pedagogical agents (PAs) during learning with MetaTutor, a hypermedia-based intelligent tutoring systems. We used eye tracking to capture visual attention to the PAs, and our results reveal specific visual attention-related metrics (e.g., fixation rate, longest fixations) that are significantly influenced by learning depending on student achievement goals. Specifically, performance-oriented students learned more with a long longest fixation and a high fixation rate on the PAs, whereas mastery-oriented students learned less with a high fixation rate on the PAs. Our findings contribute to understanding how to design PAs that can better adapt to student achievement goals and visual attention to the PA.

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Notes

  1. 1.

    The increase from pretest to post-test is statistically significant indicating that MetaTutor is overall effective at fostering learning, as further discussed in [1].

  2. 2.

    We include each of our six individual differences separately in the analysis to ensure that we do not overfit our models by including all factors at once.

  3. 3.

    We report statistical significance at the .05 level throughout this paper, and effect sizes as small for \( \eta_{\text{P}}^{2} \, \ge \,0.0 2 \), medium for \( \eta_{\text{P}}^{2} \, \ge \,0.13 \), and large for \( \eta_{\text{P}}^{2} \, \ge \,0. 2 6 \).

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Acknowledgements

This publication is based upon work supported by the National Science Foundation under Grant No. DRL-1431552 and the Social Sciences and Humanities Research Council of Canada. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Social Sciences and Humanities Research Council of Canada.

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Authors and Affiliations

  1. University of British Columbia, Vancouver, BC, Canada

    Sébastien Lallé & Cristina Conati

  2. North Carolina State University, Raleigh, NC, USA

    Michelle Taub, Nicholas V. Mudrick & Roger Azevedo

Authors
  1. Sébastien Lallé
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  2. Michelle Taub
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  3. Nicholas V. Mudrick
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  4. Cristina Conati
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  5. Roger Azevedo
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Corresponding author

Correspondence to Sébastien Lallé .

Editor information

Editors and Affiliations

  1. Human-Centered Multimedia, University of Augsburg, Augsburg, Germany

    Elisabeth André

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ryan Baker

  3. Department of Psychology, University of Memphis, Memphis, Tennessee, USA

    Xiangen Hu

  4. Ateneo de Manila University, Quezon City, Philippines

    Ma. Mercedes T. Rodrigo

  5. University of Sussex, Brighton, United Kingdom

    Benedict du Boulay

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Lallé, S., Taub, M., Mudrick, N.V., Conati, C., Azevedo, R. (2017). The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-61425-0_13

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