El confinamiento escolar por COVID19. Las TIC y ABP como garantes de los procesos de Enseñanza- Aprendizaje. Análisis de la educación en Galicia

Tesis Materias > Comunicación
Materias > Ciencias Sociales
Materias > Educación
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Tesis Doctorales Cerrado Español La historia de la educación está salpicada por episodios de confinamiento escolar, de menor o mayor duración, tal y como ocurrió en 1918 durante la pandemia de la Gripe (española) o en 1946-1965 por la pandemia provocada por la poliomielitis. Pero ninguno como el vivido durante el curso escolar 2019-2020 y su consecución en el siguiente 2020-2021, provocado por el COVID19.Y es que nunca antes la transmisión vírica tuvo una incidencia tan grande ni se expandió con tal celeridad como en este caso. La globalización, concepto acuñado en 1983 por Theodore Levitt, puede ser en gran parte el culpable del grado de expansión, rapidez y virulencia del fenómeno.En la contextualización del caso analizado en el presente estudio, cerca de 200 países han promovido el confinamiento escolar, lo que supuso que más del 80% de escolares se vieran confinados, aproximadamente cerca de 1370 millones según cifras de la UNESCO. En cuanto a la clase docente, esta, tuvo que afrontar su ejercicio profesional a distancia, desde sus hogares, lo que provocó una disrupción los los procesos de enseñanza-aprendizaje, empleando para ello TICs e infinidad de herramientas dispares, lo que provocó un abanico de problemas en su ejercicio que, no hicieron más que ahondar una brecha socio-económica-educativa ya existente, negada por la clase dirigente.En Galicia, comunidad autónoma del estado español, con competencias propias en educación, el panorama no fue diferente. El hartazgo y las quejas de los docentes acerca de la gestión de la situación, y la incertidumbre acerca de lo que les depara el futuro, motivan que el presente análisis se temporalice en tres momentos instantes: antes, durante y tras el confinamiento escolar. El fin del mismo es aportar soluciones acerca de las TIC, de su eficaz uso y de la aplicación de metodologías activas de aprendizaje, no presencial, como el ABP, ante situaciones semejantes. metadata Rial Costa, Manuel mail manuel.rial.unini@gmail.com (2022) El confinamiento escolar por COVID19. Las TIC y ABP como garantes de los procesos de Enseñanza- Aprendizaje. Análisis de la educación en Galicia. Doctoral thesis, Universidad Internacional Iberoamericana México.

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Resumen

La historia de la educación está salpicada por episodios de confinamiento escolar, de menor o mayor duración, tal y como ocurrió en 1918 durante la pandemia de la Gripe (española) o en 1946-1965 por la pandemia provocada por la poliomielitis. Pero ninguno como el vivido durante el curso escolar 2019-2020 y su consecución en el siguiente 2020-2021, provocado por el COVID19.Y es que nunca antes la transmisión vírica tuvo una incidencia tan grande ni se expandió con tal celeridad como en este caso. La globalización, concepto acuñado en 1983 por Theodore Levitt, puede ser en gran parte el culpable del grado de expansión, rapidez y virulencia del fenómeno.En la contextualización del caso analizado en el presente estudio, cerca de 200 países han promovido el confinamiento escolar, lo que supuso que más del 80% de escolares se vieran confinados, aproximadamente cerca de 1370 millones según cifras de la UNESCO. En cuanto a la clase docente, esta, tuvo que afrontar su ejercicio profesional a distancia, desde sus hogares, lo que provocó una disrupción los los procesos de enseñanza-aprendizaje, empleando para ello TICs e infinidad de herramientas dispares, lo que provocó un abanico de problemas en su ejercicio que, no hicieron más que ahondar una brecha socio-económica-educativa ya existente, negada por la clase dirigente.En Galicia, comunidad autónoma del estado español, con competencias propias en educación, el panorama no fue diferente. El hartazgo y las quejas de los docentes acerca de la gestión de la situación, y la incertidumbre acerca de lo que les depara el futuro, motivan que el presente análisis se temporalice en tres momentos instantes: antes, durante y tras el confinamiento escolar. El fin del mismo es aportar soluciones acerca de las TIC, de su eficaz uso y de la aplicación de metodologías activas de aprendizaje, no presencial, como el ABP, ante situaciones semejantes.

Tipo de Documento: Tesis (Doctoral)
Palabras Clave: TIC, aprendizaje activo, ABP, confinamiento, COVID
Clasificación temática: Materias > Comunicación
Materias > Ciencias Sociales
Materias > Educación
Divisiones: Universidad Internacional Iberoamericana Puerto Rico > Investigación > Tesis Doctorales
Depositado: 26 Sep 2023 23:30
Ultima Modificación: 26 Sep 2023 23:30
URI: https://repositorio.unib.org/id/eprint/1414

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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review

Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.

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Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.

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Client engagement solution for post implementation issues in software industry using blockchain

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Ensemble stacked model for enhanced identification of sentiments from IMDB reviews

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