Innovative pedagogies in physical education integrating technology to enhance learning outcomes
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| Publicado en: | Journal of Biotech Research vol. 21 (2025), p. 107 |
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| Autor principal: | |
| Publicado: |
Bio Tech System
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| Resumen: | The incorporation of technology into education has transformed teaching methods across multiple disciplines. Physical education (PE) that relies on conventional approaches is undergoing a paradigm shift with the adoption of creative pedagogies supported by technological tools. This shift seeks to tackle various learning styles, increase engagement, and improve results in PE. Although technology has the potential to transform PE, its influence on learning achievements is unclear with gaps in engagement, personalization, and measurable progress. This research created a new method that combined innovative pedagogies and technology to improve PE learning results by concentrating on student-specific requirements to investigate how technology interventions could be allocated and improved for enhanced engagement and efficiency. A technology-driven physical education dataset (TechPE-Data) was developed, which included features like age, fitness level, learning style, engagement level, and preferred technology. The proposed algorithm, technology-driven physical education enhancer (TechPE-Enhance), preprocessed data using K-nearest neighbors (KNN) imputation, one-hot encoding, and min-max normalization. A hybrid filter-wrapper ensemble (HFWE) was used for feature selection, which included mutual information, chi-square, ANOVA F-test, and recursive feature elimination (RFE). Soft voting was used to train an ensemble classification model that included a random forest, support vector machine (SVM), and gradient boosting machines (GBM) to allocate the best technology interventions. Furthermore, a random forest regressor predicted learning results depending on specific features. Model performance was assessed utilizing metrics like accuracy, precision, recall, F1-score, mean absolute error (MAE), and R2. The results showed that the proposed method had a classification accuracy of 92.5% with precision, recall, and F1-score of 91.8%, 92.1%, and 92.0%, respectively. The regression model achieved a MAE of 2.4 and an R2 score of 0.89, indicating high predictive capacity. Key factors such as fitness, engagement, and learning style influenced the outcomes. The study focused on technology's role in transforming PE, specifically the TechPE-Enhance algorithm for personalized and measurable learning. |
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| ISSN: | 1944-3285 |
| Fuente: | Health & Medical Collection |