Physical education and sport activity assessment tool-based machine learning predictive analysis for planification of training sessions

Background and purpose 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. Material and methods 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. Results 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. Conclusions 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.