Authors:
Haeseon Yun
1
;
Albrecht Fortenbacher
1
;
René Helbig
1
and
Niels Pinkwart
2
Affiliations:
1
University of Applied Science, Berlin and Germany
;
2
Humboldt University, Berlin and Germany
Keyword(s):
Emotion Detection, Learning Indicators, Sensor Data, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Computer-Supported Education
;
Information Technologies Supporting Learning
;
Learning Analytics
Abstract:
The goal of our research presented in this paper is to relate emotions to sensor data (heart rate and skin conductivity), to interpret them in a learning context (academic emotions) and finally derive learning indicators. For this purpose, we collected sensor data from 27 participants during an emotional picture experiment provided by IAPS (International Affective Picture System). The collected data included EDA signals (electrodermal activity), heart rate and derived data such as skin conductance response, skin conductance level, heart rate variability and instantaneous heart rate labeled by IAPS reference rating and participants’ self ratings. The processed data were analyzed using qualitative and quantitative methods as well as machine learning. Furthermore, we applied a human-machine combined approach, namely fuzzy logic reasoning. Our results show that the change of EDA when emotion is induced may serve as a feature to distinguish the intensity of emotion (arousal). Also, classi
fying EDA signals using a random forest approach shows the best accuracy. In search of learning indicators, we have attempted various tracks of analysis in this study which revealed novel findings, limitations and future steps to consider.
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