Multimodal data as a means to understand the learning experience

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

International Journal of Information Management, 2019, vol. 48, issue C, 108-119

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.

Keywords: Human learning; Multimodal learning analytics; User-generated data; Skill acquisition; Multimodal data; Machine learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:48:y:2019:i:c:p:108-119

DOI: 10.1016/j.ijinfomgt.2019.02.003

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