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An Analysis of Students’ Brain Activity when Participating in Different Learning Activities

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2020 ASEE Virtual Annual Conference Content Access


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Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Understanding Student Behavior and Experiences

Tagged Division

Educational Research and Methods

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Paper Authors


Xinyue (Crystal) Liu University of Toronto

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Crystal Liu is a graduate student at the University of Toronto in the department of Materials Science and Engineering. Her research focuses on engineering design and education. She obtained her BASc in Mechanical Engineering at the University of Toronto in 2019. She has worked in product development and is interested in application of technology and design in engineering education research.

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Yasaman Delaviz York University Orcid 16x16

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Yasaman Delaviz is an Educational/Curricular Development Specialist in the Faculty of Health at York University. She earned her PhD in Biomedical Engineering from the University of Toronto. Yasaman has extensive knowledge of curriculum design, development, and delivery and has taught numerous undergraduate-level courses at the University of Toronto and OCAD University.

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Scott D. Ramsay University of Toronto

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Scott Ramsay is an Associate Professor, Teaching Stream in the department of Materials Science and Engineering at the University of Toronto, in Toronto, Canada, and a registered professional engineer in Ontario. Scott earned his PhD in Materials Science and Engineering from the University of Toronto. Scott's current primary academic interests are in improving the quality of undergraduate engineering education through the use of various reusable learning objects. Scott has taught extensively in Material Science, teaching courses ranging from introductory materials science to thermodynamics, diffusion, materials selection, manufacturing, biomaterials, and building science.

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Title: Work In Progress – An Analysis of Students’ Physiological Activities ranging from Brainwave to Heart Rate Data when Participating in Different Learning Activities

- Existing research has demonstrated the improvement in effectiveness of learning when specific teaching methods, for example, active learning activities are used comparing with traditional passive instruction. These comparisons were usually based on measures in test scores [1][2][3]. With increasing accessibility of electroencephalography (EEG) devices, brainwave data has also been used as a means of analyzing students’ engagement during different types of learning activities [4] and determining the relationship between brain activities and test performance in small groups [5].

- This study aims to collect and analyze students’ real time brainwave data along with other physiological data while students participate in several varied learning activities, in order to better understand factors affecting students’ engagement and performance. Heart rate, heart rate variability, body temperature and skin conductivity data will be collected, in addition to EEG data.

- An off-the-shelf EEG headset will be used to collect brainwave data, specifically Alpha and Beta waves, which are related to concentration and arousal in learning activities as demonstrated in previous research [6]. This test will be conducted on five first year engineering students during lecture sessions in which traditional lecturing is used, during lectures when active learning activities are deployed, during class demonstrations, as well as during hands-on activities. Participants will be selected randomly and given the opportunity to participate in this study. Surveys will be distributed before the study to collect the information on each students’ preferred working and learning styles, as well as some demographic data.

- This study will provide baseline data that will be used in developing requirements for the design of a low-cost EEG and physiological data collection device to be deployed to a sample of approximately 30 students in a large lecture environment. Our goal is to develop a tool to provide more immediate feedback on the effectiveness of various teaching styles.

References: [1] E. Park and B. Choi, "Transformation of classroom spaces: traditional versus active learning classroom in colleges", Higher Education, vol. 68, no. 5, pp. 749-771, 2014. Available: 10.1007/s10734-014-9742-0. [2] M. Prince, "Does Active Learning Work? A Review of the Research", Journal of Engineering Education, vol. 93, no. 3, pp. 223-231, 2004. [3] S. Freeman et al., "Active learning increases student performance in science, engineering, and mathematics", Proceedings of the National Academy of Sciences, vol. 111, no. 23, pp. 8410-8415, 2014. Available: 10.1073/pnas.1319030111. [4] J. Sun, "Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data", Computers & Education, vol. 72, pp. 80-89, 2014. Available: 10.1016/j.compedu.2013.10.010. [5] D. Bevilacqua et al., "Brain-to-Brain Synchrony and Learning Outcomes Vary by Student–Teacher Dynamics: Evidence from a Real-world Classroom Electroencephalography Study", Journal of Cognitive Neuroscience, vol. 31, no. 3, pp. 401-411, 2019. Available: 10.1162/jocn_a_01274. [6] R. Loonis, S. Brincat, E. Antzoulatos and E. Miller, "A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning", Neuron, vol. 96, no. 2, pp. 521-534.e7, 2017. Available: 10.1016/j.neuron.2017.09.032.

Liu, X. C., & Delaviz, Y., & Ramsay, S. D. (2020, June), An Analysis of Students’ Brain Activity when Participating in Different Learning Activities Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34105

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