Teaching research on the cultivation of computational thinking ability by using information technology

Xiaohong, Cong; Liu, Jie; Miao, Zhibin; Xu, Li

doi:10.1109/icaie53562.2021.00125

Cited by 2 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4, enable the computation of precision, recall, and F1 score for each category of engagement as detected by RASEDS. In the realm of machine learning model evaluation, a confusion matrix serves as a pivotal tool, offering nuanced insights into the classification prowess of a model across various categories [38]. The presented matrix delineates the performance of a classifier in segregating data into five distinct classes: Interactive, Constructive, Active, Passive, and Other.…”

Section: A Evaluation Of Emotional Expression Of Studentsmentioning

confidence: 99%

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

Abdrakhmanov,

Tuimebayev,

Zhussipbek

et al. 2024

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Section: A Evaluation Of Emotional Expression Of Studentsmentioning

confidence: 99%