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

Wang Jun, Muhammad Shahid Iqbal, Rashid Abbasi, Marwan Omar, Chu Huiqin
Copyright: © 2024 |Pages: 16
DOI: 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.
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Web-Semantic-Driven Machine Learning And Blockchain For Transformative Change In The Future Of Physical Education

Physical education is an important component of a well-rounded education because it gives students the knowledge, skills, and habits they need to live a healthy and active lifestyle. Traditional physical education techniques, on the other hand, are typically one-size-fits-all and fail to take into account each student's particular needs, interests, and abilities. As a result, proponents have called for a more effective and personalized approach to physical education—one that makes use of technology to improve student performance. Physical and emotional well-being is both dependent on physical education (PE), according to R. Trigueros et al. (2019). Furthermore, research has demonstrated that physical education improves a person's psychosocial qualities (Kapsal et al., 2019). Physical education offers several health benefits, including maintaining the body's energy balance and managing the body mass index (BMI) to treat chronic noncommunicable illnesses like as obesity (Bednar & Rouse, 2020). Exercise is also necessary for the development of the cardiovascular, metabolic, musculoskeletal, and overall bodily systems. It has several advantages, and there is a strong relationship between physical exercise and health (Yiran, 2021). Incorporating physical activity into a curriculum can assist students in honing their athletic ability, developing regular physical exercise routines, and maintaining good mental health throughout time. This, in turn, improves their academic performance and prepares them for labor-market demands (Zheng Keqiang, 2022; Baena-Morales & Gonzá-Víllora, 2022). Physical education is crucial in higher education institutions, notably colleges. Despite its numerous advantages, many colleges fail to adequately expose their students to these advantages due to inadequate delivery methods. To really grasp the ideas and abilities of physical education, a modernized education system with interactive teaching methods is essential. To maximize the promise of machine learning in physical education, blockchain technology can be leveraged to offer secure and immutable storage of student data while ensuring data integrity and privacy. Physical education can adopt innovative and dependable platforms by leveraging the transparency and decentralized nature of blockchain, empowering students to take charge of their educational journey and promoting a more inclusive and collaborative educational ecosystem, as shown in Figure 1 (Liu & Li, 2023; Dziatkovskii, 2023). The potential of machine learning has resulted in a dramatic shift in the field of physical education, and teachers can now develop individualized learning experiences that meet the specific needs of each student by using machine learning algorithms to evaluate data on behavior and performance. Additionally, machine learning can be utilized to monitor and assess student progress, providing teachers with crucial data regarding the efficacy of their instructional tactics (You, 2010). Machine learning may help make physical education programming and assessments more effective and efficient.

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