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Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning – Preliminary Results from a Field Study

Li, Maximilian Xiling ORCID iD icon; Nadj, Mario; Maedche, Alexander; Ifenthaler, Dirk; Wöhler, Johannes


With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt. In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning.

Verlagsausgabe §
DOI: 10.5445/IR/1000137994
Veröffentlicht am 29.09.2021
DOI: 10.1007/s10758-021-09569-4
Zitationen: 2
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2211-1662, 2211-1670
KITopen-ID: 1000137994
Erschienen in Technology, knowledge and learning
Verlag Springer Verlag
Band 27
Seiten 365–384
Vorab online veröffentlicht am 23.09.2021
Nachgewiesen in Scopus
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