| Yazarlar |
Mustafa Altaee
|
|
A. Jawad
|
|
Mohammed Abdul Jalil
|
|
Sanaa Al-Kikani
|
|
Ahmed Oleiwi
|
Hatıra GÜNERHAN
Kafkas Üniversitesi, Türkiye |
| Özet |
| To record and evaluate students' physical education class participation, this study proposes using a Machine Learning aided Physical Training Framework (ML-PTF). Improve student achievement in physical education with the help of the Multi-level Fusion System that employs machine learning strategies. The system integrates sensor data, video data, and contextual data to deliver a holistic and precise evaluation of student engagement. This study's simulation analysis shows that the ML-PTF improves the reliability of evaluating universities' physical education programs. A important reference path and paradigm for advancing tertiary-level physical education for graduates, the multi-level fusion system also provides an investigation of information technology and language education integration. The experimental findings demonstrate that the ML-PTF is superior to other approaches in terms of learning rate, f1-score, precision, and probability, as well as student engagement, involvement, and recognition accuracy. |
| Anahtar Kelimeler |
| A Multi-level Fusion System | Assessment model | Physical Education, Machine learning | Student Activity Prediction |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SCOPUS dergilerinde yayımlanan tam makale |
| Dergi Adı | American Scientific Publishing Group |
| Dergi ISSN | 2769-786X |
| Dergi Tarandığı Indeksler | Scopus |
| Makale Dili | Türkçe |
| Basım Tarihi | 01-2023 |
| Cilt No | 9 |
| Sayı | 1 |
| Sayfalar | 8 / 23 |
| Doi Numarası | 10.54216/JISIoT.090101 |
| Makale Linki | https://doi.org/10.54216/JISIoT.090101 |
| Atıf Sayıları | |
| SCOPUS | 2 |
