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International Conference on Artificial Intelligence in Education

AIED 2019: Artificial Intelligence in Education pp 419–431Cite as

Disentangling Conceptual and Embodied Mechanisms for Learning with Virtual and Physical Representations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11625))

Abstract

Blended educational technologies offer new opportunities for students to interact with physical representations. However, it is not always clear that physical representations yield higher learning gains than virtual ones. Separate lines of prior research yield competing hypotheses about how representation modes affect learning via mechanisms of conceptual salience, embodied schemas, embodied encoding, cognitive load, and physical engagement. To test which representation modes are most effective if they differ in terms of these mechanisms, we conducted a lab experiment on chemistry learning with 119 undergraduate students. We compared four versions of energy diagrams that varied the mode and the actions students used to manipulate the representation. We tested effects on students’ learning of three concepts. Representations that induce helpful embodied schemas seem to enhance reproduction. Representations that allow for embodied encoding of haptic cues or makes concepts more salient seem to enhance transfer. Given the high costs of integrating physical representations into blended technologies, these findings may help developers focus on those learning experiences that could most be enhanced by physical interactions.

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Acknowledgements

This research was funded by NSF IIS CAREER 1651781. We thank Purav Patel and Tiffany Herder for their help with the study, and Dor Abrahamson, Matthew Dorris, Mary Hegarty, Clark Landis, John Moore, and Mike Stieff for their helpful advice.

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Authors and Affiliations

  1. University of Wisconsin – Madison, 1025 W Johnson Street, Madison, WI, 53706, USA

    Martina A. Rau & Tara A. Schmidt

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  1. Martina A. Rau
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  2. Tara A. Schmidt
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Correspondence to Martina A. Rau .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

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Rau, M.A., Schmidt, T.A. (2019). Disentangling Conceptual and Embodied Mechanisms for Learning with Virtual and Physical Representations. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_35

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  • DOI: https://doi.org/10.1007/978-3-030-23204-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23203-0

  • Online ISBN: 978-3-030-23204-7

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