Anticipating Student Engagement in Classroom through IoT-Enabled Intelligent Teaching Model Enhanced by Machine Learning

Raaid Alubady^1,*, Tamarah Alaa Diame², Hawraa Sabah³, Hasan H. Jameel Mahdi⁴, Munqith Saleem⁵, Korhan Cengiz⁶, Sahar Yassine⁷

¹Technical Engineering College, Al-Ayen University, Thi-Qar, Iraq

²Technical Computer Engineering Department, Al-Kunooze University College, Basrah, Iraq

³Department Of Medical Devices Engineering Technologies, National University Of Science And Technology, Dhi Qar, Nasiriyah, Iraq

⁴Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq

⁵Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad, Iraq;

⁶Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Kralove, 500 03, Czech Republic

⁷Department of Applied Data Science, Noroff University College, Kristiansand, Norway;

Emails: Alubadyraaid@Alayen.Edu.Iq; Tamarah.Alaa@Kunoozu.Edu.Iq; Hawraa.A.Sabah@Nust.Edu.Iq;

Hasan.Harith@Turath.Edu.Iq; Munqith.Saleem@Uoalfarahidi.Edu.Iq; korhan.cengiz@uhk.cz; sahar.yassine@noroff.no

^*Corresponding Author: Alubadyraaid@Alayen.Edu.Iq

Abstract

Machine learning provides several advantages for the usage of physical teaching technology. Machine learning is one of the major paths with connected technology and is part of a powerful frontier discipline that develops and influences overall education growth. To enhance student connection and assess student involvement in physical education, the Machine Learning assisted Computerized Physical Teaching Model (MLCPTM) has been developed in this work. The proposed MLCPTM intends to investigate and address contemporary technical physical education to create the ideal theoretical foundation for the growth of technology and current physical activity. Virtual reality (VR) technologies are used in the proposed MLCPTM to create a system for correcting physical education activity. The theory and category of machine learning were covered in this essay, along with a thorough analysis and examination of modern technological advancements in physical education. The challenges with machine learning in contemporary sports instructional technologies are also explained. Then, athletes should accelerate their knowledge of the movement techniques and heighten the training effect. According to the results of the experiments, the suggested MLCPTM model outperforms other existing models in terms of an effective learning ratio of 82.5 per cent, feedback ratio of 96 per cent, response ratio of 98.6 per cent, decision-making ratio of 96.3 per cent, and movement detection ratio of 79.84 per cent, the precision ratio of 97.8 per cent.

Keywords: Correction System; Machine Learning; Physical Education Classroom; Physical Activity; Student Involvement.