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Toward a Multi-dimensional Biometric Approach to Quantifying Student Engagement in the STEM Classroom

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Computers in Education Division Technical Session 2: Teaching and Learning

Tagged Division

Computers in Education

Page Count

14

DOI

10.18260/1-2--35392

Permanent URL

https://peer.asee.org/35392

Download Count

137

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Paper Authors

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James Christopher Foreman University of Louisville Orcid 16x16 orcid.org/0000-0001-6756-2890

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Asst. Professor at University of Louisville, previous appointment at Purdue University. Teaching calculus, power and energy, and industrial control systems related courses. Research in artificial neural networks, expert systems, and new methods of teaching math/calculus. 15 years in industry control systems and power generation industry prior to academic career.

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Aly Farag University of Louisville

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Aly Farag, Fellow, IEEE and IAPR: received B.S. in EE from Cairo Univ. M.S. in Bioengineering from the Ohio State and the Univ. of Michigan, and PhD in EE from Purdue. He is a Prof. of ECE at the Univ. of Louisville, and director of the Computer Vision & Image Processing Laboratory, focusing on research and teaching in computer vision, biometrics and biomedical imaging. He introduced over 13 new courses into the ECE curriculum, authored over 400 papers, edited two volumes on deformable models and a textbook on Biomedical Image Analysis (Cambridge Univ. Press, 2014). He graduated over 70 MS and PhD students, and mentored over 20 postdoctoral researchers. He holds seven US patents on object modeling, computer-aided diagnosis, and visualization. He was lead editor of IEEE-TIFS special issue on Face Recognition in the Wild (December 2014), and co-general chair of ICIP-2009. He is recipient of the University top Awards: Research (1999), Teaching (2009, 2011) and Trustees (2015).

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Asem Ali University of Louisville Orcid 16x16 orcid.org/0000-0002-0503-4838

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Asem M. Ali received the M.S. degree in electrical engineering from Assiut University, Asyut, Egypt, in 2002, and the Ph.D. degree in computer engineering from the University of Louisville, Louisville, KY, USA, in 2008, where he was a Post-Doctoral Researcher with the Computer Vision and Image Processing Laboratory from 2008 to 2011. He was an Assistant Professor with the Department of Electrical Engineering, Assiut University from 2011 to 2015. He is currently a Research Scientist with the Computer Vision and Image Processing Laboratory. His research interests include image analysis, machine learning, face recognition, and facial expressions and emotions recognition. He has authored over 40 papers in journals and conferences.

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Islam Alkabbany University of Louisville

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Marci S. DeCaro University of Louisville Orcid 16x16 orcid.org/0000-0001-6753-0725

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Marci DeCaro is an Associate Professor in the Department of Psychological and Brain Sciences at the University of Louisville. Dr. DeCaro earned a PhD in cognitive science from Miami University in 2009 and a US Department of Education-funded postdoctoral fellowship at Vanderbilt University from 2009-2011. She directs the Learning and Performance Lab at the University of Louisville. Her research examines the cognitive mechanisms underlying learning and problem solving in both laboratory and educational contexts, including STEM classrooms.

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Thomas Tretter University of Louisville

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Thomas Tretter is professor of science education and director of the Gheens Science Hall & Rauch Planetarium at the University of Louisville. His scholarship includes collaborative efforts with science and engineering faculty targeting retention of STEM majors in entry-level STEM courses.

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Abstract

An approach to performing a quantitative and objective measurement of student engagement in the classroom is proposed. The approach observes biometric data from the students and is multi-dimensional in that it incorporates facial expressions and hand/head/body movement captured by camera, in addition to pulse and blood pressure captured by a wristband device. From these data, a machine-learning model is trained to classify student engagement. Engagement is classified from behavioral, emotional, and cognitive aspects. The capability to measure student engagement can be used by the instructor to tailor the presentation of material in class, identify course material that engages and disengages with students, and identify students that are disengaged and at risk of failure. Further, this capability allows quantitative comparison of teaching methods, such as lecture, flipped classrooms, classroom response systems, etc. such that an objective metric is used to close the loop on method evaluation. This is a work in progress, supported by NSF funds, applied to first-year engineering mathematics courses.

Foreman, J. C., & Farag, A., & Ali, A., & Alkabbany, I., & DeCaro, M. S., & Tretter, T. (2020, June), Toward a Multi-dimensional Biometric Approach to Quantifying Student Engagement in the STEM Classroom Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35392

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