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      Sensor Measures of Affective Leaning

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          Abstract

          The aim of this study was to predict self-report data for self-regulated learning with sensor data. In a longitudinal study multichannel data were collected: self-report data with questionnaires and embedded experience samples as well as sensor data like electrodermal activity (EDA) and electroencephalography (EEG). 100 students from a private university in Germany performed a learning experiment followed by final measures of intrinsic motivation, self-efficacy and gained knowledge. During the learning experiment psychophysiological data like EEG were combined with embedded experience sampling measuring motivational states like affect and interest every 270 s. Results of machine learning models show that consumer grade wearables for EEG and EDA failed to predict embedded experience sampling. EDA failed to predict outcome measures as well. This gap can be explained by some major technical difficulties, especially by lower quality of the electrodes. Nevertheless, an average activation of all EEG bands at T7 (left-hemispheric, lateral) can predict lower intrinsic motivation as outcome measure. This is in line with the personality system interactions (PSI) theory of Julius Kuhl. With more advanced sensor measures it might be possible to track affective learning in an unobtrusive way and support micro-adaptation in a digital learning environment.

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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                30 April 2020
                2020
                : 11
                : 379
                Affiliations
                [1] 1Medical School Hamburg , Hamburg, Germany
                [2] 2Institute of Information Systems, Leuphana University , Lüneburg, Germany
                Author notes

                Edited by: Andreas Gegenfurtner, University of Passau, Germany

                Reviewed by: Giovanna Bubbico, G. d’Annunzio University of Chieti and Pescara, Italy; Leen Catrysse, University of Antwerp, Belgium

                *Correspondence: Thomas Martens, thomas.martens@ 123456medicalschool-hamburg.de

                This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2020.00379
                7203482
                e5a8577a-938e-489c-aeac-ab28abb972dc
                Copyright © 2020 Martens, Niemann and Dick.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 July 2019
                : 18 February 2020
                Page count
                Figures: 3, Tables: 7, Equations: 0, References: 58, Pages: 10, Words: 0
                Funding
                Funded by: Bundesministerium für Bildung und Forschung 10.13039/501100002347
                Award ID: 16SV7517SH
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                sensor measures,process measures,affect,emotion,motivation,eeg,affective learning,self-regulated learning

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