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International Conference on Artificial Intelligence in Education

AIED 2019: Artificial Intelligence in Education pp 432–444Cite as

Adaptive Support for Representation Skills in a Chemistry ITS Is More Effective Than Static Support

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

Abstract

Multiple visual representations can enhance learning in STEM, provided that students have prerequisite representation skills to make sense of how the visuals show information and to fluently perceive meaning in the visuals. Prior research shows that instructional support for sense-making skills and perceptual fluency enhances STEM learning. This research also shows that students need different types of support, depending on their prior representation skills. Hence, instruction may be most effective if it adaptively assigns students to support for sense-making skills and perceptual fluency. We tested this hypothesis in an experiment with 45 undergraduates in an introductory chemistry course. Students were randomly assigned to a 6-week instructional module of an intelligent tutoring system (ITS) that (1) provided a static sequence of activities that supported sense-making skills and perceptual fluency or (2) adaptively assigned the activities. Results show that the adaptive version yielded significantly higher gains of chemistry knowledge. Our findings expand theories of representation skills and yield recommendations for ITSs with multiple visual representations.

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Acknowledgements

This research was funded by NSF DUE-IUSE 1611782. We thank John Moore and Matthew Dorris for their advice and help with this study.

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Authors and Affiliations

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

    Martina A. Rau, Miranda Zahn, Edward Misback & Judith Burstyn

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  1. Martina A. Rau
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  2. Miranda Zahn
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  3. Edward Misback
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  4. Judith Burstyn
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Corresponding author

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., Zahn, M., Misback, E., Burstyn, J. (2019). Adaptive Support for Representation Skills in a Chemistry ITS Is More Effective Than Static Support. 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_36

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  • DOI: https://doi.org/10.1007/978-3-030-23204-7_36

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  • Online ISBN: 978-3-030-23204-7

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