Virtual On line
June 22, 2020
June 22, 2020
June 26, 2021
Computers in Education
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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