Teaching Games for Understanding (TGfU) un método de enseñanza comprensiva en educación física: Revi-sión Sistemática de los últimos 5 años (Teaching Games for Understanding (TGfU) as comprehensive teaching method in physical education: System-atic review of the last 5 years)
Bibliographic record
Abstract
Durante décadas la educación física ha implementado diversos acercamientos o modelos pedagógicos como herramientas para la mejora de su práctica educativa y la implementación de un método de enseñanza comprensiva como el Teaching Games for Understanding ha sido una de estas formas. Sin embargo, existe actualmente un debate con respecto a cuál será el mejor acercamiento implementado en las clases de educación física y México no ha sido la excepción. El propósito de esta revisión sistemática de los últimos 5 años es analizar un acercamiento como herramienta de trabajo para que docentes de educación física de diferentes niveles en México puedan implementar. A través de la metodología PRISMA se identificaron un total de 562 artículos, una vez aplicados los criterios de inclusión, quedaron 25 artículos como elegibles, aunque solo 12 cumplieron con estos criterios. Los resultados muestran que la implementación del Teaching Games for Understanding es una forma eficaz y eficiente para su uso docente en las clases de educación física. Su aplicación ha sido mayormente en países desarrollados como España, Canadá y Estados Unidos. La combinación con algún otro método también muestra beneficios y ser una forma atractiva para que niños y adolescentes participen en clase. Por lo que podemos decir que esta herramienta pedagógica puede ser un aliciente para que docentes de educación física en México lo implementen con éxito. Siendo esta un área de oportunidad para un estudio comprensivo y epistemológico mayor del uso de este acercamiento en las sesiones de educación física en México. Palabras Clave: Método enseñanza, Educación física, Revisión Sistemática, Docentes Abstract. Over decades physical education has implemented different pedagogical approaches or models as tools to improve the educational practice and the implementation of a comprehensive teaching method as the Teaching Games for Understanding has been one of those forms. However, there is a current debate with respect what would be the best approach to be implemented in the physical education classes and Mexico has not been the exception. The purpose of this systematic review from the last 5 years is to analyze an approach as pedagogical tool that physical education teachers from different levels in Mexico could implement. Through the PRISMA methodology there were 562 articles identifies, once the inclusion criteria were applied, only 25 articles were eligible, although only 12 completed the inclusion criteria. The results show that implementing the Teaching Games for Understanding is an effective and efficient to be used by teachers within the physical education classes. Its implementation has been predominantly in developed countries such as Spain, Canada and the United States. The combination with another method has also shown to be an attractive way so that children and youth participate in class. As such, it can be said that this pedagogical tool could be an incentive to implemented with success by physical education teachers in Mexico. Being an area of opportunity to an in-depth and epistemological study in the use of this approach in physical education classes in Mexico. Keywords: Teaching Methods, Physical education, Systematic review, Teachers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".