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Multimodal data as a means to understand the learning experience

Author

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  • Giannakos, Michail N.
  • Sharma, Kshitij
  • Pappas, Ilias O.
  • Kostakos, Vassilis
  • Velloso, Eduardo

Abstract

Most work in the design of learning technology uses click-streams as their primary data source for modelling & predicting learning behaviour. In this paper we set out to quantify what, if any, advantages do physiological sensing techniques provide for the design of learning technologies. We conducted a lab study with 251 game sessions and 17 users focusing on skill development (i.e., user's ability to master complex tasks). We collected click-stream data, as well as eye-tracking, electroencephalography (EEG), video, and wristband data during the experiment. Our analysis shows that traditional click-stream models achieve 39% error rate in predicting learning performance (and 18% when we perform feature selection), while for fused multimodal the error drops up to 6%. Our work highlights the limitations of standalone click-stream models, and quantifies the expected benefits of using a variety of multimodal data coming from physiological sensing. Our findings help shape the future of learning technology research by pointing out the substantial benefits of physiological sensing.

Suggested Citation

  • Giannakos, Michail N. & Sharma, Kshitij & Pappas, Ilias O. & Kostakos, Vassilis & Velloso, Eduardo, 2019. "Multimodal data as a means to understand the learning experience," International Journal of Information Management, Elsevier, vol. 48(C), pages 108-119.
    • Handle: RePEc:eee:ininma:v:48:y:2019:i:c:p:108-119
    DOI: 10.1016/j.ijinfomgt.2019.02.003
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    Cited by:

    1. Gandhi, Mohina & Kar, Arpan Kumar, 2022. "How do Fortune firms build a social presence on social media platforms? Insights from multi-modal analytics," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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