%0 Journal Article %1 Li2021 %A Li, Maximilian Xiling %A Nadj, Mario %A Maedche, Alexander %A Ifenthaler, Dirk %A Wöhler, Johannes %D 2021 %J Technology, Knowledge and Learning %K flow infrastructure learninganalytics machinelearning motivation physiologicalcomputing remotelearning %R 10.1007/s10758-021-09569-4 %T Towards a Physiological Computing Infrastructure for Researching Students' Flow in Remote Learning %U https://doi.org/10.1007/s10758-021-09569-4 %X 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.

@article{Li2021, abstract = {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.}, added-at = {2021-10-03T12:37:57.000+0200}, author = {Li, Maximilian Xiling and Nadj, Mario and Maedche, Alexander and Ifenthaler, Dirk and W{\"o}hler, Johannes}, biburl = {https://www.bibsonomy.org/bibtex/2790bc0fe7c8c2ec48cedae4b7191dd4f/ereidt}, day = 23, doi = {10.1007/s10758-021-09569-4}, interhash = {80a3e0e6d12474b71733ad7e66574550}, intrahash = {790bc0fe7c8c2ec48cedae4b7191dd4f}, issn = {2211-1670}, journal = {Technology, Knowledge and Learning}, keywords = {flow infrastructure learninganalytics machinelearning motivation physiologicalcomputing remotelearning}, month = sep, timestamp = {2021-10-03T12:37:57.000+0200}, title = {Towards a Physiological Computing Infrastructure for Researching Students' Flow in Remote Learning}, url = {https://doi.org/10.1007/s10758-021-09569-4}, year = 2021 }