Las competencias docentes en su formación de pregrado: un estudio del perfil profesional para la acción pedagógica en educación básica en la ciudad de Soacha-Colombia
Artículo Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés, Español Este artículo recoge los datos de la investigación hecha en la ciudad de Soacha, Colombia, sobre las competencias que adquirieron los docentes en su formación de pregrado y que laboran en el nivel de educación básica. Pretende indicar en un análisis de tipo mixto, las fortalezas y oportunidades, así como las debilidades y amenazas, en referencia a: las competencias adquiridas, en un grupo muestra de 50 docentes a través de instrumentos como la encuesta y la entrevista, competencias que son necesarias en el perfil profesional que propone las políticas educativas nacionales. Los datos, nos dejará ver, un diagnóstico sobre el porcentaje de competitividad frente a los requerimientos del estado colombiano, el cual pretende para el año 2025 alcanzar una excelencia educativa, como mejor país en los procesos de educación en Latinoamérica. El estudio nos muestra el perfil real del docente, en el nivel educativo de pregrado y sus fortalezas y falencias a la hora de las prácticas como profesional, así como el acercamiento a los perfiles que solicita el estado. De igual modo, dará pautas para que las instituciones que apliquen la metodología, puedan desde la implementación y el análisis de la propuesta, proyectar planes de mejora en la formación del recurso humano que participa en el desarrollo del Proyecto Educativo Institucional con el cual se pretende alcanzar mayor calidad educativa, como también el proponer articulaciones formativas y de mejora, con las universidades de las cuales han egresado los docentes y que incursionan en el ambiente educativo de la ciudad de Soacha. metadata Acuña Gamboa, Luis Alan y Suárez Ramírez, Marco Aurelio mail SIN ESPECIFICAR, marco.suarez@doctorado.unini.edu.mx (2022) Las competencias docentes en su formación de pregrado: un estudio del perfil profesional para la acción pedagógica en educación básica en la ciudad de Soacha-Colombia. MLS Educational Research, 6 (1). ISSN 2603-5820
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Este artículo recoge los datos de la investigación hecha en la ciudad de Soacha, Colombia, sobre las competencias que adquirieron los docentes en su formación de pregrado y que laboran en el nivel de educación básica. Pretende indicar en un análisis de tipo mixto, las fortalezas y oportunidades, así como las debilidades y amenazas, en referencia a: las competencias adquiridas, en un grupo muestra de 50 docentes a través de instrumentos como la encuesta y la entrevista, competencias que son necesarias en el perfil profesional que propone las políticas educativas nacionales. Los datos, nos dejará ver, un diagnóstico sobre el porcentaje de competitividad frente a los requerimientos del estado colombiano, el cual pretende para el año 2025 alcanzar una excelencia educativa, como mejor país en los procesos de educación en Latinoamérica. El estudio nos muestra el perfil real del docente, en el nivel educativo de pregrado y sus fortalezas y falencias a la hora de las prácticas como profesional, así como el acercamiento a los perfiles que solicita el estado. De igual modo, dará pautas para que las instituciones que apliquen la metodología, puedan desde la implementación y el análisis de la propuesta, proyectar planes de mejora en la formación del recurso humano que participa en el desarrollo del Proyecto Educativo Institucional con el cual se pretende alcanzar mayor calidad educativa, como también el proponer articulaciones formativas y de mejora, con las universidades de las cuales han egresado los docentes y que incursionan en el ambiente educativo de la ciudad de Soacha.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | competencias, pregrado, formación, calidad educativa, perfil docente |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Artículos y libros |
| Depositado: | 17 Oct 2022 23:30 |
| Ultima Modificación: | 17 Oct 2022 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/4060 |
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Carotenoids Intake and Cardiovascular Prevention: A Systematic Review
Background: Cardiovascular diseases (CVDs) encompass a variety of conditions that affect the heart and blood vessels. Carotenoids, a group of fat-soluble organic pigments synthesized by plants, fungi, algae, and some bacteria, may have a beneficial effect in reducing cardiovascular disease (CVD) risk. This study aims to examine and synthesize current research on the relationship between carotenoids and CVDs. Methods: A systematic review was conducted using MEDLINE and the Cochrane Library to identify relevant studies on the efficacy of carotenoid supplementation for CVD prevention. Interventional analytical studies (randomized and non-randomized clinical trials) published in English from January 2011 to February 2024 were included. Results: A total of 38 studies were included in the qualitative analysis. Of these, 17 epidemiological studies assessed the relationship between carotenoids and CVDs, 9 examined the effect of carotenoid supplementation, and 12 evaluated dietary interventions. Conclusions: Elevated serum carotenoid levels are associated with reduced CVD risk factors and inflammatory markers. Increasing the consumption of carotenoid-rich foods appears to be more effective than supplementation, though the specific effects of individual carotenoids on CVD risk remain uncertain.
Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Thomas Prola mail thomas.prola@uneatlantico.es, Raquel Martínez Díaz mail raquel.martinez@uneatlantico.es, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es,
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The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.
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Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.
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