Multimodal data as a means to understand the learning experience
Michail N Giannakos, Kshitij Sharma, Ilias O Pappas, Vassilis Kostakos, Eduardo Velloso
International Journal of Information Management | Pergamon Press | Published : 2019
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 perfo..View full abstract
Awarded by European Union
Awarded by Norwegian Research Council
The authors would like to thank all the participants of this study. This work was carried out during the tenure of an ERCIM "Alain Bensoussan" Fellowship Programme. This work has received funding from the European Union's Horizon 2020 research and innovation programme, under the Marie Sklodowska-Curie grant agreement No. 751510, and by the Norwegian Research Council under the project FUTURE LEARNING (number: 255129/H20) and Xdesign (290994/F20).