Aplicação das praticas educativas na inclusão social diante das competências digitais inseridas no contexto do ensino remoto durante o Covid Dissertação para obtenção do grau de: Mestre em Educação - Especialista em Formação de Professores Apresentado por: Elza Stauber BRFPMME2993626 Orientadora: Profa. Claudia Catanõ; Dra. São Paulo, Brasil 29 de abril de 2022.
Thesis
Subjects > Teaching
Europe University of Atlantic > Teaching > Final Master Projects
Ibero-american International University > Teaching > Final Master Projects
Cerrado
Portugués
O papel estratégico da educação como alavanca de crescimento e desenvolvimento socioeconômico tornou-se um consenso internacional hoje. Além de democratizar as oportunidades educacionais, a educação deve ser garantida de qualidade, que prepara os cidadãos para serem críticos na vida social participativa, permitindo-lhes entrar e permanecer no mercado de trabalho em constante mudança. Desencadearam debates sobre educação e mudanças, discutiu a urgência de reformulação do projeto de ensino, quanto custa a formação, atuação e desenvolvimento das pessoas que nela ensinam? Nesse sentido, a possibilidade de usar a tecnologia como ferramenta, dessa maneira o ensino provou ser um caminho promissor, e o objetivo de ambos ao ter o alcance objetivos educacionais que satisfaça interesses e as necessidades de professores e alunos mercado. Esta pesquisa inclui pesquisas aplicadas sobre prática educacional e avaliação de competências digitais no contexto da educação a distância para professores da Etec – Raposo Tavares, Brasil. Os métodos de desenvolvimento de currículo são baseados em pesquisas que usam técnicas de análise de dados qualitativos para determinar o potencial do professor e a ação de pesquisa para participar de projetos. Com o apoio das contribuições legais atuais, Moran (2005), Luckesi (2005) (e as contribuições de Alcântara (2018) e Oliveira-Torres (2012)) serão agregados a esta pesquisa. O presente estudo constitui-se de uma pesquisa justaposta com entrevistas direcionadas a estudantes e professores, que apresentou como objetivos em analisar e avaliar as práticas educativas adotadas pelos professores da ETEC escola técnica de Raposo Tavares – SP , frente às mudanças nas estratégias didáticas e nos critérios avaliativos devido à implantação do ensino remoto emergencial. Os resultados demonstram a necessidade em desenvolver ações pedagógicas que incitem mudanças de expectativa entre professores ao refletir alterações em suas visões, valores e crenças, com relação ao desempenho dos alunos e sobre como devem realizar essa nova tarefa de avaliar e criar estratégias para os professores intervirem nas práticas com suas avaliações e as competências digitais no ensino remoto.
metadata
Stauber, Elza
mail
elzastauber@yahoo.com.br
(2022)
Aplicação das praticas educativas na inclusão social diante das competências digitais inseridas no contexto do ensino remoto durante o Covid Dissertação para obtenção do grau de: Mestre em Educação - Especialista em Formação de Professores Apresentado por: Elza Stauber BRFPMME2993626 Orientadora: Profa. Claudia Catanõ; Dra. São Paulo, Brasil 29 de abril de 2022.
Masters thesis, UNSPECIFIED.
Abstract
O papel estratégico da educação como alavanca de crescimento e desenvolvimento socioeconômico tornou-se um consenso internacional hoje. Além de democratizar as oportunidades educacionais, a educação deve ser garantida de qualidade, que prepara os cidadãos para serem críticos na vida social participativa, permitindo-lhes entrar e permanecer no mercado de trabalho em constante mudança. Desencadearam debates sobre educação e mudanças, discutiu a urgência de reformulação do projeto de ensino, quanto custa a formação, atuação e desenvolvimento das pessoas que nela ensinam? Nesse sentido, a possibilidade de usar a tecnologia como ferramenta, dessa maneira o ensino provou ser um caminho promissor, e o objetivo de ambos ao ter o alcance objetivos educacionais que satisfaça interesses e as necessidades de professores e alunos mercado. Esta pesquisa inclui pesquisas aplicadas sobre prática educacional e avaliação de competências digitais no contexto da educação a distância para professores da Etec – Raposo Tavares, Brasil. Os métodos de desenvolvimento de currículo são baseados em pesquisas que usam técnicas de análise de dados qualitativos para determinar o potencial do professor e a ação de pesquisa para participar de projetos. Com o apoio das contribuições legais atuais, Moran (2005), Luckesi (2005) (e as contribuições de Alcântara (2018) e Oliveira-Torres (2012)) serão agregados a esta pesquisa. O presente estudo constitui-se de uma pesquisa justaposta com entrevistas direcionadas a estudantes e professores, que apresentou como objetivos em analisar e avaliar as práticas educativas adotadas pelos professores da ETEC escola técnica de Raposo Tavares – SP , frente às mudanças nas estratégias didáticas e nos critérios avaliativos devido à implantação do ensino remoto emergencial. Os resultados demonstram a necessidade em desenvolver ações pedagógicas que incitem mudanças de expectativa entre professores ao refletir alterações em suas visões, valores e crenças, com relação ao desempenho dos alunos e sobre como devem realizar essa nova tarefa de avaliar e criar estratégias para os professores intervirem nas práticas com suas avaliações e as competências digitais no ensino remoto.
Item Type: | Thesis (Masters) |
---|---|
Uncontrolled Keywords: | Ações pedagógicas, Aprendizagem, Avaliações, Ensino Remoto. |
Subjects: | Subjects > Teaching |
Divisions: | Europe University of Atlantic > Teaching > Final Master Projects Ibero-american International University > Teaching > Final Master Projects |
Date Deposited: | 16 Nov 2023 23:30 |
Last Modified: | 16 Nov 2023 23:30 |
URI: | https://repositorio.unini.edu.mx/id/eprint/1814 |
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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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Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography
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