Multimodal data as a means to understand the learning experience |
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| Institution: | 1. Department of Computer and Information Science, Norwegian University of Science and, Technology (NTNU), Sem Sælands vei 7-9, 7491, Trondheim, Norway;2. School of Computing & Information Systems, University of Melbourne, Room 9.03, Doug McDonell Building 168, VIC-3010 Parkville, Australia;3. University of Agder (UiA), Kristiansand, Norway |
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| 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. |
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| Keywords: | Human learning Multimodal learning analytics User-generated data Skill acquisition Multimodal data Machine learning |
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