Abstract
It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it’s still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student’s classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student’s learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.
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Data availability
The datasets generated for this study are available on request to the corresponding author.
Abbreviations
- TTP:
-
Trough-to-peak
- SRL:
-
Self-regulated learning
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Acknowledgements
We thank the school staff at Tianjin University and the Biomedical Engineering students for all their time, support, and enthusiasm. We thank Beijing Huixin Technology Co., Ltd for their wearable EDA recording devices and technical support.
Funding
This research was funded in part by the National Key Research and Development Program of China (Grant No. STI 2030-Major Projects 2022ZD0208900), National Natural Science Foundation of China (Grant No. 62122059, 61976152, 81925020, 62106170), Introduce Innovative Teams of 2021 “New High School 20 Items” Project (2021GXRC071).
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HY conducted the experiment. HY, MX, and DM contributed equally to the study conception, data analyses, and writing. All authors contributed to the manuscript revision, and read, and approved the submitted version.
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This study was approved by the Ethics Committee of Tianjin University. (Number: TJUE-2021–180).
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Yu, H., Xu, M., Xiao, X. et al. Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students. Cogn Neurodyn 18, 173–184 (2024). https://doi.org/10.1007/s11571-023-09930-6
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DOI: https://doi.org/10.1007/s11571-023-09930-6