Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use

William L. Romine; Noah L. Schroeder; Josephine Graft; Fan Yang; Reza Sadeghi; Mahdieh Zabihimayvan; Dipesh Kadariya; Tanvi Banerjee

doi:10.3390/s20174833

Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use

William L. Romine, Noah L. Schroeder, Josephine Graft, Fan Yang, Reza Sadeghi, Mahdieh Zabihimayvan, Dipesh Kadariya, Tanvi Banerjee

Biological Sciences

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	4833
Pages (from-to)	1-14
Number of pages	14
Journal	Sensors
Volume	20
Issue number	17
DOIs	https://doi.org/10.3390/s20174833
State	Published - Sep 1 2020

ASJC Scopus Subject Areas

Analytical Chemistry
Information Systems
Atomic and Molecular Physics, and Optics
Biochemistry
Instrumentation
Electrical and Electronic Engineering

Keywords

Cognitive load
Learning analytics
Machine learning
Studying
Wearable sensor

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10.3390/s20174833

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Romine, W. L., Schroeder, N. L., Graft, J., Yang, F., Sadeghi, R., Zabihimayvan, M., Kadariya, D., & Banerjee, T. (2020). Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use. Sensors, 20(17), 1-14. Article 4833. https://doi.org/10.3390/s20174833

Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use. / Romine, William L.; Schroeder, Noah L.; Graft, Josephine et al.
In: Sensors, Vol. 20, No. 17, 4833, 01.09.2020, p. 1-14.

Research output: Contribution to journal › Article › peer-review

Romine, WL, Schroeder, NL, Graft, J, Yang, F, Sadeghi, R, Zabihimayvan, M, Kadariya, D & Banerjee, T 2020, 'Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use', Sensors, vol. 20, no. 17, 4833, pp. 1-14. https://doi.org/10.3390/s20174833

Romine WL, Schroeder NL, Graft J, Yang F, Sadeghi R, Zabihimayvan M et al. Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use. Sensors. 2020 Sep 1;20(17):1-14. 4833. doi: 10.3390/s20174833

Romine, William L. ; Schroeder, Noah L. ; Graft, Josephine et al. / Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate : Development of a cognitive load tracker for both personal and classroom use. In: Sensors. 2020 ; Vol. 20, No. 17. pp. 1-14.

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Using machine learning to train a wearable device for measuring students’ cognitive load during problem-solving activities based on electrodermal activity, body temperature, and heart rate: Development of a cognitive load tracker for both personal and classroom use

ASJC Scopus Subject Areas

Keywords

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