Formación del profesorado de danza en Paraguay: propuesta de un modelo educativo basado en teorías cognitivo-constructivistas
Tesis Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Español La presente tesis realiza un estudio de la situación actual de la danza en Paraguay y del programa de formación de los docentes en danza para finalizar con la propuesta de un modelo educativo basado en teorías cognitivo-constructivistas. La enseñanza de la danza en Paraguay está unificada, sistematizada y reglamentada a través de un único programa a nivel nacional. Esto permite a las academias de danzas privadas otorgar títulos profesionales con un aval oficial del Ministerio de Educación y Ciencias, por intermedio de la Dirección General de Educación en el Arte. El programa de estudios acompaña la educación formal y la evolución de los estudiantes desde los 7 hasta los 18 años, al término del cual, los educandos obtienen el título de Profesor Superior de Danzas. En el presente trabajo se recurrió a la revisión bibliográfica sobre temas inherentes a éste como el paradigma socio-crítico, la investigación-acción, la danza en un contexto científico, la investigación en el arte, educación en el arte, currículo y modelos educativos de manera a sustentar la propuesta a ser presentada. Con la finalidad de ahondar más en la situación actual de la danza en Paraguay, se realizó una investigación acción enmarcada dentro del paradigma sociocrítico aplicando como técnica de recolección de datos la revisión de documentos, entrevistas, encuestas y observaciones que arrojaron como resultado que el correr del tiempo y los cambios realizados en la estructura de la danza, entre otros factores, hicieron que esta estructura formal esté atravesando por una etapa difícil. Para paliar esta situación se realiza la propuesta de un modelo educativo basado en teorías cognitivo-constructivistas con el propósito de mejorar la formación de los docentes en danza y mantener la estructura que guía ese proceso metadata Espínola Torres, Lydia Marcela mail maleloni@gmail.com (2020) Formación del profesorado de danza en Paraguay: propuesta de un modelo educativo basado en teorías cognitivo-constructivistas. Doctoral thesis, Universidad Internacional Iberoamericana México.
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La presente tesis realiza un estudio de la situación actual de la danza en Paraguay y del programa de formación de los docentes en danza para finalizar con la propuesta de un modelo educativo basado en teorías cognitivo-constructivistas. La enseñanza de la danza en Paraguay está unificada, sistematizada y reglamentada a través de un único programa a nivel nacional. Esto permite a las academias de danzas privadas otorgar títulos profesionales con un aval oficial del Ministerio de Educación y Ciencias, por intermedio de la Dirección General de Educación en el Arte. El programa de estudios acompaña la educación formal y la evolución de los estudiantes desde los 7 hasta los 18 años, al término del cual, los educandos obtienen el título de Profesor Superior de Danzas. En el presente trabajo se recurrió a la revisión bibliográfica sobre temas inherentes a éste como el paradigma socio-crítico, la investigación-acción, la danza en un contexto científico, la investigación en el arte, educación en el arte, currículo y modelos educativos de manera a sustentar la propuesta a ser presentada. Con la finalidad de ahondar más en la situación actual de la danza en Paraguay, se realizó una investigación acción enmarcada dentro del paradigma sociocrítico aplicando como técnica de recolección de datos la revisión de documentos, entrevistas, encuestas y observaciones que arrojaron como resultado que el correr del tiempo y los cambios realizados en la estructura de la danza, entre otros factores, hicieron que esta estructura formal esté atravesando por una etapa difícil. Para paliar esta situación se realiza la propuesta de un modelo educativo basado en teorías cognitivo-constructivistas con el propósito de mejorar la formación de los docentes en danza y mantener la estructura que guía ese proceso
Tipo de Documento: | Tesis (Doctoral) |
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Palabras Clave: | Modelo educativo; teorías cognitivo-constructivistas; educación en danza; formación del profesorado; investigación-acción; paradigma sociocrítico |
Clasificación temática: | Materias > Educación |
Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales |
Depositado: | 11 Mar 2022 23:55 |
Ultima Modificación: | 20 Sep 2023 23:30 |
URI: | https://repositorio.unini.edu.mx/id/eprint/550 |
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The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
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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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