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Socially, politically, and morally, the world of sport is still changing. On the other hand, technology has been the most prevalent transition in the sport over the last century. Thanks to modern science, athletes can now go higher, run quicker, and, most importantly, remain healthy. Although academics, agencies, and policymakers had already urged physical education teachers to use technology in their classrooms, in many of these situations, technology is used for administrative purposes, including tracking enrolment and measuring, documenting, and reporting students’ work. Thus, this paper suggests an intelligent Student Actions Evaluation System using Deep Learning (iSAES-DL)for student monitoring in physical education. This model uses the deep convolution neural network for the classification of risky actions. This model further evaluates the learners’ degree of learning, retention, and achievements and suggests improvements and corrective measures. It highlights the benefits, uses, and limitations of applying deep learning techniques and IoT devices to develop learning analytics systems in the physical, educational domain. Eventually, output criteria such as comprehension, concentration, retention, and learner attainment are given a feature-by-feature analysis of the proposed methodology and traditional teaching-learning approaches. Finally, the classification algorithm is contrasted to other deep learning algorithms with an F1-score of 97.86%.

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.

While Learning Analytics has been widely used to improve learning experiences such as course content, activities, and assessments, it plays a significant role in providing data-driven insight into the efficacy of a program. This chapter sheds light on how big data can be used in curriculum development to ensure that the skills and competencies students learn at educational institutions align with those required in the current and future job market. By exploring three case studies in which big data has been utilized to revise and update curricula in different fields, this chapter suggests that big data allows curriculum designers to make data-driven decisions which leads to a higher rate of employability and satisfaction among students. This chapter also discusses the limitations and challenges of using big data in education.