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The driving concept of students’ sports training involves a unique activity that is often tightly correlated to students’ efficiency and varies with the momentum of sports training. Supervised learning is one of the smart methods with positive results in the fields of classification techniques. Due to the excessive currency unit associated with sports, sports forecasting is a growing area that must be well predicted. Therefore, in this paper, sports training based on the supervised learning (STSLM) model has been proposed to evaluate and predict student sports efficiency. STSLM models are based on various variables, such as traditional student ratings, performance, and efficiency. The emphasis is on the efficiency of students predicting sports outcomes. STSLM defines evaluation methods, information sources, effective models for testing students’ sports training, and unique challenges to forecast sports outcomes. The experimental results have been performed. The suggested STSLM model enhances the efficiency ratio of 96.3%, injury prevention level of 98.2%, fitness level of 95.5%, evaluation ratio of 98.8%, and training optimization ratio of 97.2% compared to other existing approaches.

Group work can inspire students, encourage constructive learning, and improve essential critical thinking, communication, and decision-making in the present competitive world. The risk factors in group learning include students who prefer working alone and strongly despise dealing with things created by gathering in teams. Online learners often have problems locating lasting peace times for group therapy sessions are considered an essential factor. A predictive Group Learning Behavior Approach (PGLBA) has been proposed. Students who commute to college agree to group conferences and workgroup learning and Inquiry learning for higher education. The grounded Inquiry Learning Approach is invented to strengthen students’ enjoyment of active group learning, and the students find times for group meetings that are often mutually advantageous. The simulation analysis is performed based on performance, accuracy, and efficiency proves the proposed framework’s reliability. The experimental results show that the proposed PGLBA-IL model enhances the accuracy ratio of 81.2%, an efficiency ratio of the number of students 86.4%, and the overall performance analysis ratio of 85.1% compared to others existing approaches.