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

Wang Jun, Muhammad Shahid Iqbal, Rashid Abbasi, Marwan Omar, Chu Huiqin

Source Title: International Journal on Semantic Web and Information Systems (IJSWIS)20(1)

ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9798369324684|DOI: 10.4018/IJSWIS.337961

MLA

Jun, Wang, et al. "Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education." IJSWIS vol.20, no.1 2024: pp.1-16. http://doi.org/10.4018/IJSWIS.337961

APA

Jun, W., Iqbal, M. S., Abbasi, R., Omar, M., & Huiqin, C. (2024). Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-16. http://doi.org/10.4018/IJSWIS.337961

Chicago

Jun, Wang, et al. "Web-Semantic-Driven Machine Learning and Blockchain for Transformative Change in the Future of Physical Education," International Journal on Semantic Web and Information Systems (IJSWIS) 20, no.1: 1-16. http://doi.org/10.4018/IJSWIS.337961

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Abstract

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.