Community College Institutional Researchers’ Knowledge, Experience, and Perceptions of Machine Learning

Scull, W. Reed; Perkins, Mark A.; Carrier, Jonathan W.; Barber, Michael J.

doi:10.1080/10668926.2022.2043202

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…This research contributes to this ongoing scholarly dialogue, contextualizing the application of CV within the specific challenges and opportunities presented by self-directed STEM education [29][30][31][32]. Through an interdisciplinary lens, this study seeks to build upon the foundations laid by existing literature [33][34][35][36][37], proposing an innovative convergence of CV technology and educational pedagogy to enhance the quality and effectiveness of self-study in STEM disciplines.…”

Section: Related Workmentioning

confidence: 99%

Applying Computer Vision and Machine Learning Techniques in STEM-Education Self-Study

Abdrakhmanov,

Tuimebayev,

Zhussipbek

et al. 2024

IJACSA

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In this innovative exploration, "Applying Computer Vision Techniques in STEM-Education Self-Study," the research delves into the transformative intersection of advanced computer vision (CV) technologies and self-directed learning within Science, Technology, Engineering, and Mathematics (STEM) education. Challenging traditional educational paradigms, this study posits that sophisticated CV algorithms, when judiciously integrated with modern educational frameworks, can profoundly augment the efficacy of self-study models for students navigating the increasingly intricate STEM curricula. By leveraging state-of-the-art facial recognition, object detection, and pattern analysis, the study underscores how CV can monitor, analyze, and thereby enhance students' engagement and interaction with digital content, a pioneering stride that addresses the prevalent disconnect between static study materials and the dynamic nature of learner engagement. Furthermore, the research illuminates the critical role of CV in generating personalized study roadmaps, effectively responding to individual learner's behavioral patterns and cognitive absorption rhythms, identified through meticulous analysis of captured visual data, thereby transcending the one-size-fits-all educational approach. Through rigorous qualitative and quantitative research methods, the paper offers groundbreaking insights into students' study habits, proclivities, and the nuanced obstacles they face, facilitating the creation of responsive, adaptive, and deeply personalized learning experiences. Conclusively, this research serves as a clarion call to educators, technologists, and policy-makers, emphatically demonstrating that the thoughtful application of computer vision techniques not only catalyzes a more engaging self-study landscape but also holds the latent potential to revolutionize the holistic STEM education ecosystem.

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