[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122255-en":3,"doc-seo-122255-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122255,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",4,"Exam","Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions","Study integrates machine learning techniques into physical education activity assessment to enable planning of training sessions and learning cycles via predictive analyses. The dataset combines physical tests (Harvard test, vertical and horizontal trigger) and athletic performance outcomes (600 m, 1000 m, 12 min Cooper) collected from 600 secondary school students aged 15–20 during 2021–2022, then projects predicted results for learning cycles in 2022–2023. Microsoft Azure Machine Learning Studio is used to identify the best model using R2 as evaluation metric. Results and conclusions highlight promising prediction quality and key features influencing students’ performance.","Health, sport, rehabilitation Здоров’я, спорт, реабілітаціяЗдоровье, спорт, реабилитация  \n2024  \n10(3)  \n\n| ORIGINAL ARTICLES. PHYSICAL EDUCATION |\n| --- |\n| Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions\u003Cbr>Mohamed Rebbouj 1ABCD, Said Lotfi 1BCD\u003Cbr>1 Multidisciplinary Laboratory In Education Sciences and Training Engeneering (LMSEIF) . Sport Science Assessment and Physical Activity Didactic. Normal Higher School (ENS-C), Hassan II\u003Cbr>University of Casablanca, Morocco\u003Cbr>Authors’ Contribution: A – Study design; B – Data collection; C – Statistical analysis; D – Manuscript Preparation; E – Funds Collection\u003Cbr>DOI: [https://doi.org/10.58962/HSR.2024.10.3.95-104](https://doi.org/10.58962/HSR.2024.10.3.95-104)\u003Cbr>Corresponding Author: Mohamed Rebbouj, [Mohamed.rebbouj@ensaca.ma](Mohamed.rebbouj@ensaca.ma), [https://orcid.org/0000-0002-](https://orcid.org/0000-0002-)[ ](https://orcid.org/0000-0002-)[7725-4979](7725-4979), Department of physical education and sport. Multidisciplinary laboratory in education sciences and training engineering (LMSEIF), sport science assessment and physical activity didactic. Normal Higher School (ENS-C) of Casablanca. Hassan II university of Casablanca. BP 50069, Ghandi. Morocco |\n| How to Cite\u003Cbr>Rebbouj M, Lotfi S. Physical education and sport activity assessment tool-based Machine Learning predictive analysis for planification of training sessions. Health, Sport, Rehabilitation. 2024;10(3):95-104 . [https://doi](https://doi). org/10.58962/HSR.2024.10.3.95-104\u003Cbr> |\n| Abstract\u003Cbr>Background and purpose\u003Cbr>The aim of this study is to incorporte machine learning techniques in physical education activities assessment so we can plan a training session and learning cycle based on predictive analyses using machine learning algorithms.\u003Cbr>Material and methods\u003Cbr>A dataset represent the collection of physical tests (as Harvard test, Vertical and Horizontal Trigger) and activities performance (as 600 m, 1000 m, 12 min cooper) of 600 students in a secondary high school, aged between 15 and 20 years old (mean:16,21, SD:0,92), during 2021-2022 scholar year and project the predicted results on the following learning cycles in the scholar year of 2022-2023. We used Microsoft Azure Machine Learning Studio to obtain the best predictive model based on R2 score as an evaluating metric.\u003Cbr>Results\u003Cbr>Even if we focus on one metric test (as a target) with numeric values in this article, the results were promising compared to the predicted values of both physical tests and athletic performances, where we noticed some students have exceeded the expected values to reach. And the predictive analysis unveiled the more important features impacting the predicted results for the physical test.\u003Cbr>Conclusions\u003Cbr>Incorporating the Machine Learning techniques may encourage the change in the way we teach physical education and sport activities; otherwise, the assessment based on ML techniques will give a different overview on how to start a learning cycle and follow it up. The obtained predictive model provides an explication of the most impacting features on students’ performance allowing any training planification to relay on their importance respectively based on their density that affects prediction.\u003Cbr>Keywords: physical education assessment, physical tests, athletic performance, machine learning |\n\n© Rebbouj M, Lotfi S, 2024. [https://doi.org/10.58962/](https://doi.org/10.58962/)[ ](https://doi.org/10.58962/)HSR.2024.10.3.95-104  \n95  \nThis work is licensed under a Creative  Commons Attribution 4.0 International  License (CC BY 4 .0)  \nHealth, sport, rehabilitation Здоров’я, спорт, реабілітаціяЗдоровье, спорт, реабилитация  \n2024  \n10(3)  \nАнотація  \nМохамед Реббудж, Саїд Лотфі. Прогнозний аналіз інструментів оцінки фізичного виховання та спортивноїактивності на основі машинного навчання для планування тренувань  \nОбгрунтування і мета  ","cbCaiiZqMeqUhTnN","https://ap.wps.com/l/cbCaiiZqMeqUhTnN","pdf",728750,1,10,"English","en",105,"# Abstract\n## Background and purpose\n## Material and methods\n## Results\n## Conclusions\n# Keywords","[{\"question\":\"What is the aim of this study?\",\"answer\":\"The study aims to incorporate machine learning techniques into physical education activity assessment to plan training sessions and learning cycles using predictive analyses.\"},{\"question\":\"What data and prediction approach were used?\",\"answer\":\"The dataset includes physical tests (Harvard test, vertical and horizontal trigger) and performance results (600 m, 1000 m, 12 min Cooper) from 600 students, with predictions projected for subsequent learning cycles using Microsoft Azure Machine Learning Studio.\"},{\"question\":\"What do the results show about predictive analysis?\",\"answer\":\"The predictive analysis produced promising results compared with predicted values for both physical tests and athletic performances, and it identified important features that influence predicted outcomes.\"}]","Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions | 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