Avaliação dos impactos do Desing Thinking na prática pedagógica, no rendimento escolar e na criatividades de estudantes da Educação Básica
Tesis Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Portugués Esta pesquisa teve como objetivo avaliar os impactos do uso do Design Thinking na prática pedagógica, no rendimento escolar e na criatividade de estudantes dos anos finais do Ensino Fundamental. Metodologicamente, o estudo se caracteriza como uma pesquisa avaliativa, centrada na abordagem quantitativa. A população foi composta por 250 estudantes que frequentavam os anos finais do Ensino Fundamental. Desses, foram selecionados por acessibilidade 141 estudantes de quatro turmas do 6° ao 9° do período matutino. Para a coleta de dados, optou-se pelos seguintes instrumentos: Questionário sobre Potencial Criativo (QPC) aplicado no início e no final da pesquisa; Questionário sobre Expectativas Discentes sobre a Prática Pedagógica (QEDPP) aplicado no início da pesquisa; Questionário sobre a Aplicabilidade do Design Thinking (QADT) aplicado ao final do estudo; um formulário de Comparação do Rendimento Escolar (FCRE) para coleta de dados no início e no final do estudo. Em relação a situações que refletem no rendimento das disciplinas, indicaram, no pré-teste, a dificuldade de concentração em aula e, entre as dificuldades para o bom rendimento, a forma de apresentação do conteúdo. Após a aplicação do Design Thinking, observaram-se melhoras no rendimento dos estudantes em todas as disciplinas. Em relação às possibilidades de inovações propostas, as opções com maior frequência indicaram a organização da sala de aula em diferentes formatos, os debates sobre temas contemporâneos, os desafios em grupo, a discussão dos programas e as atividades no celular. Também destacarem o potencial do Design Thinking para ampliar a compreensão dos conteúdos, ampliar a motivação de frequentar a escola, bem como para a mudança no desempenho docente. Esses apontamentos são parte dos resultados, sendo que os demais se pretendem apresentar na fase de finalização da tese. metadata Kuschnaroff Contreras, Fernando mail contrerasfernandok@gmail.com (2022) Avaliação dos impactos do Desing Thinking na prática pedagógica, no rendimento escolar e na criatividades de estudantes da Educação Básica. Doctoral thesis, Universidad Internacional Iberoamericana México.
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Esta pesquisa teve como objetivo avaliar os impactos do uso do Design Thinking na prática pedagógica, no rendimento escolar e na criatividade de estudantes dos anos finais do Ensino Fundamental. Metodologicamente, o estudo se caracteriza como uma pesquisa avaliativa, centrada na abordagem quantitativa. A população foi composta por 250 estudantes que frequentavam os anos finais do Ensino Fundamental. Desses, foram selecionados por acessibilidade 141 estudantes de quatro turmas do 6° ao 9° do período matutino. Para a coleta de dados, optou-se pelos seguintes instrumentos: Questionário sobre Potencial Criativo (QPC) aplicado no início e no final da pesquisa; Questionário sobre Expectativas Discentes sobre a Prática Pedagógica (QEDPP) aplicado no início da pesquisa; Questionário sobre a Aplicabilidade do Design Thinking (QADT) aplicado ao final do estudo; um formulário de Comparação do Rendimento Escolar (FCRE) para coleta de dados no início e no final do estudo. Em relação a situações que refletem no rendimento das disciplinas, indicaram, no pré-teste, a dificuldade de concentração em aula e, entre as dificuldades para o bom rendimento, a forma de apresentação do conteúdo. Após a aplicação do Design Thinking, observaram-se melhoras no rendimento dos estudantes em todas as disciplinas. Em relação às possibilidades de inovações propostas, as opções com maior frequência indicaram a organização da sala de aula em diferentes formatos, os debates sobre temas contemporâneos, os desafios em grupo, a discussão dos programas e as atividades no celular. Também destacarem o potencial do Design Thinking para ampliar a compreensão dos conteúdos, ampliar a motivação de frequentar a escola, bem como para a mudança no desempenho docente. Esses apontamentos são parte dos resultados, sendo que os demais se pretendem apresentar na fase de finalização da tese.
| Tipo de Documento: | Tesis (Doctoral) |
|---|---|
| Palabras Clave: | Desing Thinking, Prática Pedagógica, Rendimento Escolar, Interdisciplinaidade, Transdisciplinaridade, Paradigma Emergente, Pensamento Complexo,Criatividade |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales |
| Depositado: | 26 Sep 2023 23:30 |
| Ultima Modificación: | 26 Sep 2023 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/1293 |
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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.
Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,
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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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Khouili
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Measurement of chest muscle mass in COVID-19 patients on mechanical ventilation using tomography
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Alzheimer's disease (AD) involves β-amyloid plaques and tau hyperphosphorylation, driven by oxidative stress and neuroinflammation. Cyclooxygenase-2 (COX-2) and acetylcholinesterase (AChE) activities exacerbate AD pathology. Olive leaf (OL) extracts, rich in bioactive compounds, offer potential therapeutic benefits. This study aimed to assess the anti-inflammatory, anti-cholinergic, and antioxidant effects of three OL extracts (low, mid, and high bioactive content) in vitro and their protective effects against AD-related proteinopathies in Caenorhabditis elegans models. OL extracts were characterized for phenolic composition, AChE and COX-2 inhibition, as well as antioxidant capacity. Their effects on intracellular and mitochondrial reactive oxygen species (ROS) were tested in C. elegans models expressing human Aβ and tau proteins. Gene expression analyses examined transcription factors (DAF-16, skinhead [SKN]-1) and their targets (superoxide dismutase [SOD]-2, SOD-3, GST-4, and heat shock protein [HSP]-16.2). High-OL extract demonstrated superior AChE and COX-2 inhibition and antioxidant capacity. Low- and high-OL extracts reduced Aβ aggregation, ROS levels, and proteotoxicity via SKN-1/NRF-2 and DAF-16/FOXO pathways, whereas mid-OL showed moderate effects through proteostasis modulation. In tau models, low- and high-OL extracts mitigated mitochondrial ROS levels via SOD-2 but had limited effects on intracellular ROS levels. High-OL extract also increased GST-4 levels, whereas low and mid extracts enhanced GST-4 levels. OL extracts protect against AD-related proteinopathies by modulating oxidative stress, inflammation, and proteostasis. High-OL extract showed the most promise for nutraceutical development due to its robust phenolic profile and activation of key antioxidant pathways. Further research is needed to confirm long-term efficacy.
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Garlic is a horticultural product highly valued for its culinary and medicinal attributes. The aim of this study was to evaluate the composition of a garlic hydrophilic extract as well as the influence on redox biology, Alzheimer's Disease (AD) markers and aging, using Caenorhabditis elegans as experimental model. The extract was rich in sulfur compounds, highlighting the presence of other compounds like phenolics, and the antioxidant property was corroborated. Regarding AD markers, the acetylcholinesterase inhibitory capacity was demonstrated in vitro. Although the extract did not modify the amyloid β-induced paralysis degree, it was able to improve, in a dose-dependent manner, some locomotive parameters affected by the hyperphosphorylated tau protein in C. elegans. It could be related to the effect found on GFP-transgenic stains, mainly regarding to the increase in the gene expression of HSP-16.2. Moreover, an initial investigation into the aging process revealed that the extract successfully inhibited the accumulation of intracellular and mitochondrial reactive oxygen species in aged worms. These results provide valuable insights into the multifaceted impact of garlic extract, particularly in the context of aging and neurodegenerative processes. This study lays a foundation for further research avenues exploring the intricate molecular mechanisms underlying garlic effects and its translation into potential therapeutic interventions for age-related neurodegenerative conditions.
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