Sports Ed 3.5: Establishing the value of data-driven sports development programs for universities through machine learning models

Torralba, Edwin M.

doi:10.1145/3446999.3447009

Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart City 2020

DOI: 10.1145/3446999.3447009

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Sports Ed 3.5: Establishing the value of data-driven sports development programs for universities through machine learning models

Edwin M. Torralba

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Cited by 2 publications

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“…The study recommends an insightful actual school system that offers individualized and enrapturing actual instruction encounters by using web of things and AI innovation (Wang & Du, 2022). In this work, information on understudy lead and execution are examined, utilizing AI to upgrade the plan of the actual schooling educational program (Mendoza Torralba, 2020). This review sheds light on the social elements impacting understudy execution by anticipating actual schooling grades utilizing AI and informal organization investigation (Taş et al, 2021).…”

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Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education

Jun,

Iqbal,

Abbasi

et al. 2024

International Journal on Semantic Web and Information Systems

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Machine learning is playing an increasingly important role in education. This article examines its potential to bring about transformative change in this field. By using machine learning algorithms, physical education teachers can gather and analyze data on student performance and behavior. This enables them to create personalized learning experiences that cater to the unique needs of each student. Machine learning can also track and assess student progress, providing educators with valuable insights into the effectiveness of their teaching strategies. Furthermore, it can optimize the design of physical education curricula and assessments, making them more efficient and effective. Additionally, machine learning offers a more objective and accurate approach to evaluating and grading students. This paper discusses the challenges and opportunities associated with integrating machine learning into physical education, including ethical considerations and potential limitations.

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confidence: 99%

Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education

Jun,

Iqbal,

Abbasi

et al. 2024

International Journal on Semantic Web and Information Systems

View full text Add to dashboard Cite

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Examining the Relationship of Teacher and Peer Belonging to Rural Attachment and Community Aspirations Among Diverse Rural Youth

Irdam,

Starrett,

Irvin

et al. 2023

The Rural Educator

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Sports Ed 3.5: Establishing the value of data-driven sports development programs for universities through machine learning models

Cited by 2 publications

References 19 publications

Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education

Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education

Examining the Relationship of Teacher and Peer Belonging to Rural Attachment and Community Aspirations Among Diverse Rural Youth

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