Multi-Label Classification Computer Vision Models to Measure Online Classroom Engagement

Autor/in	Davidson, Jason L.
Titel	Multi-Label Classification Computer Vision Models to Measure Online Classroom Engagement
Quelle	(2023), (144 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Indiana State University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-3795-3652-7
Schlagwörter	Hochschulschrift; Dissertation; Automation; Online Courses; Nonverbal Communication; Learner Engagement; Educational Technology; Synchronous Communication; Psychological Patterns; Models; College Students; Measurement + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Online course; Online-Kurs; Non-verbal communication; Nonverbale Kommunikation; Unterrichtsmedien; Analogiemodell; Collegestudent; Messverfahren
Abstract	Student enrollment in online courses has nearly tripled over the last decade, with 72% of college students participating in at least one online course. There are many advantages to online education such as increased classroom diversity, the reduction of geographical limitations, and overall convenience. However, studies have shown students participating in online courses underperform when compared to their counterparts in traditional face-to-face classroom settings. Reasons for these discrepancies include limited supervision, lack of resources, feelings of isolation, and low engagement. The challenge of student engagement is exacerbated by educators' reduced ability to visually assess students' nonverbal cues of the affective states (engagement, boredom, confusion, frustration). This research seeks to address the challenge of reduced nonverbal communication by automating student engagement recognition with computer vision. Modern deep learning techniques will be used to create a convolutional neural network to measure the affective states of students in a synchronous online environment. Additionally, this project will explore the correlation, if any, between the affective states and seven universal emotional states of anger, contempt, disgust, fear, joy, sadness, and surprise. The ultimate goal is to develop a real-time monitor that indicates to online educators the level of each student's engagement during a synchronous online course. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01