Leveraging Computer Vision for Adaptive Learning in STEM Education: Effect of Engagement and Self-Efficacy

Autor/inn/en	Wu, Ting-Ting; Lee, Hsin-Yu; Wang, Wei-Sheng; Lin, Chia-Ju; Huang, Yueh-Min
Titel	Leveraging Computer Vision for Adaptive Learning in STEM Education: Effect of Engagement and Self-Efficacy
Quelle	In: International Journal of Educational Technology in Higher Education, 20 (2023), Artikel 53 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wu, Ting-Ting) ORCID (Lee, Hsin-Yu) ORCID (Wang, Wei-Sheng) ORCID (Lin, Chia-Ju) ORCID (Huang, Yueh-Min)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1186/s41239-023-00422-5
Schlagwörter	STEM Education; Learner Engagement; Self Efficacy; Problem Solving; Artificial Intelligence; Identification; Interaction; Learning Processes; Computer Assisted Instruction + Suchen Sie Ihr Suchwort? STEM; Self-efficacy; Selbstwirksamkeit; Problemlösen; Künstliche Intelligenz; Identifikation; Identifizierung; Interaktion; Learning process; Lernprozess; Computer based training; Computerunterstützter Unterricht
Abstract	In the field of Science, Technology, Engineering, and Mathematics (STEM) education, which aims to cultivate problem-solving skills, accurately assessing learners' engagement remains a significant challenge. We present a solution to this issue with the Real-time Automated STEM Engagement Detection System (RASEDS). This innovative system capitalizes on the power of artificial intelligence, computer vision, and the Interactive, Constructive, Active, and Passive (ICAP) framework. RASEDS uses You Only Learn One Representation (YOLOR) to detect and map learners' interactions onto the four levels of engagement delineated in the ICAP framework. This process informs the system's recommendation of adaptive learning materials, designed to boost both engagement and self-efficacy in STEM activities. Our study affirms that RASEDS accurately gauges engagement, and that the subsequent use of these adaptive materials significantly enhances both engagement and self-efficacy. Importantly, our research suggests a connection between elevated self-efficacy and increased engagement. As learners become more engaged in their learning process, their confidence is bolstered, thereby augmenting self-efficacy. We underscore the transformative potential of AI in facilitating adaptive learning in STEM education, highlighting the symbiotic relationship between engagement and self-efficacy. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01