Educação inclusiva: a importância e seus desafios
Tesis Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster Cerrado Portugués O presente estudo tem como objetivo investigar as mudanças na prática docente após o curso de formação continuada para uso de TIC com foco na educação especial buscando identificar se esses saberes têm contribuído para a prática pedagógica e para o crescimento profissional de professores atuantes na educação inclusiva nas escolas estaduais do município de Urucurituba – AM.Esta pesquisa foi desenvolvida a partir de uma metodologia qualitativa com abordagem etnográfica. Os procedimentos que nortearam esta pesquisa empírica foram de forma online devido à pandemia Covid-19. O trabalho foi desenvolvido com professores do Ensino Fundamental nos anos finais, num total de sete docentes. A investigação foi feita através de questionários, estas pelo aplicativo WhatsApp alguns por intermédio de material impresso, direcionada aos professores que acompanham o processo pedagógico dos alunos com necessidades especiais nas salas regulares. A análise dos dados foi realizada a partir de abordagem qualitativa, considerando a técnica de Análise de Conteúdo. Os resultados desta pesquisa demonstraram que os professores enfrentam como principais desafios a falta de infraestrutura e recursos materiais para lidar com o processo de inclusão escolar, além de destacarem a falta de capacitação para tanto. A formação continuada que realizaram para uso da TIC com alunos com necessidades especiais lhes possibilitou melhor entendimento sobre o que era realizado com os alunos na sala de AEE, todavia, como os recursos não são suficientes, não conseguem aplicar na sua prática. Verificou-se a necessidade de uma formação continuada que demonstre aos professores a possibilidade de uso dos seus próprios smartphones para inclusão escolar, mostrando que o processo pode ser simples, assim, foi sugerida uma formação continuada de 6 meses que envolve o uso simplificado de tecnologias e a facilitação do planejamento das aulas, já que esta também foi apontada como uma dificuldade dos professores, que relataram não conseguir adequar as aulas considerando a realidade de cada aluno.Ao final do estudo foi possível verificar a importância das SRM para o desenvolvimento integral dos alunos com necessidades especiais atendidos no AEE e, consequente, para a promoção da efetivação da inclusão escolar dessas crianças, com a manipulação de materiais adequados para o seu aprendizado, além de profissionais devidamente capacitados para lidar com suas necessidades. metadata Vieira Costa, Nilton mail niltoncosta.vieira@hotmail.com (2022) Educação inclusiva: a importância e seus desafios. Masters thesis, SIN ESPECIFICAR.
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O presente estudo tem como objetivo investigar as mudanças na prática docente após o curso de formação continuada para uso de TIC com foco na educação especial buscando identificar se esses saberes têm contribuído para a prática pedagógica e para o crescimento profissional de professores atuantes na educação inclusiva nas escolas estaduais do município de Urucurituba – AM.Esta pesquisa foi desenvolvida a partir de uma metodologia qualitativa com abordagem etnográfica. Os procedimentos que nortearam esta pesquisa empírica foram de forma online devido à pandemia Covid-19. O trabalho foi desenvolvido com professores do Ensino Fundamental nos anos finais, num total de sete docentes. A investigação foi feita através de questionários, estas pelo aplicativo WhatsApp alguns por intermédio de material impresso, direcionada aos professores que acompanham o processo pedagógico dos alunos com necessidades especiais nas salas regulares. A análise dos dados foi realizada a partir de abordagem qualitativa, considerando a técnica de Análise de Conteúdo. Os resultados desta pesquisa demonstraram que os professores enfrentam como principais desafios a falta de infraestrutura e recursos materiais para lidar com o processo de inclusão escolar, além de destacarem a falta de capacitação para tanto. A formação continuada que realizaram para uso da TIC com alunos com necessidades especiais lhes possibilitou melhor entendimento sobre o que era realizado com os alunos na sala de AEE, todavia, como os recursos não são suficientes, não conseguem aplicar na sua prática. Verificou-se a necessidade de uma formação continuada que demonstre aos professores a possibilidade de uso dos seus próprios smartphones para inclusão escolar, mostrando que o processo pode ser simples, assim, foi sugerida uma formação continuada de 6 meses que envolve o uso simplificado de tecnologias e a facilitação do planejamento das aulas, já que esta também foi apontada como uma dificuldade dos professores, que relataram não conseguir adequar as aulas considerando a realidade de cada aluno.Ao final do estudo foi possível verificar a importância das SRM para o desenvolvimento integral dos alunos com necessidades especiais atendidos no AEE e, consequente, para a promoção da efetivação da inclusão escolar dessas crianças, com a manipulação de materiais adequados para o seu aprendizado, além de profissionais devidamente capacitados para lidar com suas necessidades.
| Tipo de Documento: | Tesis (Masters) |
|---|---|
| Palabras Clave: | Inclusão Escolar,Formação Continuada,Tecnologias. |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster |
| Depositado: | 08 Nov 2023 23:30 |
| Ultima Modificación: | 08 Nov 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/1755 |
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Background: Scientific research should be carried out to prevent sports injuries. For this purpose, new assessment technologies must be used to analyze and identify the risk factors for injury. The main objective of this systematic review was to compile, synthesize and integrate international research published in different scientific databases on Countermovement Jump (CMJ), Functional Movement Screen (FMS) and Tensiomyography (TMG) tests and technologies for the assessment of injury risk in sport. This way, this review determines the current state of the knowledge about this topic and allows a better understanding of the existing problems, making easier the development of future lines of research. Methodology: A structured search was carried out following the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and the PICOS model until November 30, 2024, in the MEDLINE/PubMed, Web of Science (WOS), ScienceDirect, Cochrane Library, SciELO, EMBASE, SPORTDiscus and Scopus databases. The risk of bias was assessed and the PEDro scale was used to analyze methodological quality. Results: A total of 510 articles were obtained in the initial search. After inclusion and exclusion criteria, the final sample was 40 articles. These studies maintained a high standard of quality. This revealed the effects of the CMJ, FMS and TMG methods for sports injury assessment, indicating the sample population, sport modality, assessment methods, type of research design, study variables, main findings and intervention effects. Conclusions: The CMJ vertical jump allows us to evaluate the power capacity of the lower extremities, both unilaterally and bilaterally, detect neuromuscular asymmetries and evaluate fatigue. Likewise, FMS could be used to assess an athlete's basic movement patterns, mobility and postural stability. Finally, TMG is a non-invasive method to assess the contractile properties of superficial muscles, monitor the effects of training, detect muscle asymmetries, symmetries, provide information on muscle tone and evaluate fatigue. Therefore, they should be considered as assessment tests and technologies to individualize training programs and identify injury risk factors.
Álvaro Velarde-Sotres mail alvaro.velarde@uneatlantico.es, Antonio Bores-Cerezal mail antonio.bores@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Julio Calleja-González mail ,
Velarde-Sotres
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In the rapidly evolving landscape of artificial intelligence (AI) and the Internet of Things (IoT), the significance of device diagnostics and prognostics is paramount for guaranteeing the dependable operation and upkeep of intricate systems. The capacity to precisely diagnose and preemptively predict potential failures holds the potential to considerably amplify maintenance efficiency, diminish downtime, and optimize resource allocation. The wealth of information offered by telemetry data gathered from IoT devices presents an opportunity for diagnostics and prognostics applications. However, extracting valuable insights and making well-timed decisions from this extensive data reservoir remains a formidable challenge. This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. The proposed methodology utilizes rule-based inference to detect real-time anomalies and predict potential future failures, providing a dual-layered approach for diagnostics and prognostics. The results show that the diagnostics engine using forward chaining detects real-time issues like “High Temperature” and “Low Pressure,” while the prognostics engine with backward chaining predicts potential future occurrences of these issues, enabling proactive prevention measures. The experimental results demonstrate that adopting this approach could offer valuable assistance to authorities and stakeholders. Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.
Muhammad Shoaib Farooq mail , Rizwan Pervez Mir mail , Atif Alvi mail , Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Fadwa Alrowais mail , Hanen Karamti mail , Imran Ashraf mail ,
Farooq
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Efficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into predefined categories. This research presents a unique feature identification technique based on suppression to locate interest points by computing productive sum of pixel derivatives by computing the differentials for corner scores. Scale space interpolation is applied to define interest points by combining color features from spatially ordered L2 normalized coefficients with shape and object information. Object based feature vectors are formed using high variance coefficients to reduce the complexity and are converted into bag-of-visual-words (BoVW) for effective retrieval and ranking. The presented method encompass feature vectors for information synthesis and improves the discriminating strength of the retrieval system by extracting deep image features including primitive, spatial, and overlayed using multilayer fusion of Convolutional Neural Networks(CNNs). Extensive experimentation is performed on standard image datasets benchmarks, including ALOT, Cifar-10, Corel-10k, Tropical Fruits, and Zubud. These datasets cover wide range of categories including shape, color, texture, spatial, and complicated objects. Experimental results demonstrate considerable improvements in precision and recall rates, average retrieval precision and recall, and mean average precision and recall rates across various image semantic groups within versatile datasets. The integration of traditional feature extraction methods fusion with multilevel CNN advances image sensing and retrieval systems, promising more accurate and efficient image retrieval solutions.
Jyotismita Chaki mail , Aiza Shabir mail , Khawaja Tehseen Ahmed mail , Arif Mahmood mail , Helena Garay mail helena.garay@uneatlantico.es, Luis Eduardo Prado González mail uis.prado@uneatlantico.es, Imran Ashraf mail ,
Chaki
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Nut Consumption Is Associated with Cognitive Status in Southern Italian Adults
Background: Nut consumption has been considered a potential protective factor against cognitive decline. The aim of this study was to test whether higher total and specific nut intake was associated with better cognitive status in a sample of older Italian adults. Methods: A cross-sectional analysis on 883 older adults (>50 y) was conducted. A 110-item food frequency questionnaire was used to collect information on the consumption of various types of nuts. The Short Portable Mental Status Questionnaire was used to assess cognitive status. Multivariate logistic regression analyses were performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between nut intake and cognitive status after adjusting for potential confounding factors. Results: The median intake of total nuts was 11.7 g/day and served as a cut-off to categorize low and high consumers (mean intake 4.3 g/day vs. 39.7 g/day, respectively). Higher total nut intake was significantly associated with a lower prevalence of impaired cognitive status among older individuals (OR = 0.35, CI 95%: 0.15, 0.84) after adjusting for potential confounding factors. Notably, this association remained significant after additional adjustment for adherence to the Mediterranean dietary pattern as an indicator of diet quality, (OR = 0.32, CI 95%: 0.13, 0.77). No significant associations were found between cognitive status and specific types of nuts. Conclusions: Habitual nut intake is associated with better cognitive status in older adults.
Justyna Godos mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Evelyn Frias-Toral mail , Raynier Zambrano-Villacres mail , Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Vanessa Yélamos Torres mail vanessa.yelamos@funiber.org, Maurizio Battino mail maurizio.battino@uneatlantico.es, Fabio Galvano mail , Sabrina Castellano mail , Giuseppe Grosso mail ,
Godos
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Novel transfer learning approach for hand drawn mathematical geometric shapes classification
Hand-drawn mathematical geometric shapes are geometric figures, such as circles, triangles, squares, and polygons, sketched manually using pen and paper or digital tools. These shapes are fundamental in mathematics education and geometric problem-solving, serving as intuitive visual aids for understanding complex concepts and theories. Recognizing hand-drawn shapes accurately enables more efficient digitization of handwritten notes, enhances educational tools, and improves user interaction with mathematical software. This research proposes an innovative machine learning algorithm for the automatic classification of mathematical geometric shapes to identify and interpret these shapes from handwritten input, facilitating seamless integration with digital systems. We utilized a benchmark dataset of mathematical shapes based on a total of 20,000 images with eight classes circle, kite, parallelogram, square, rectangle, rhombus, trapezoid, and triangle. We introduced a novel machine-learning algorithm CnN-RFc that uses convolution neural networks (CNN) for spatial feature extraction and the random forest classifier for probabilistic feature extraction from image data. Experimental results illustrate that using the CnN-RFc method, the Light Gradient Boosting Machine (LGBM) algorithm surpasses state-of-the-art approaches with high accuracy scores of 98% for hand-drawn shape classification. Applications of the proposed mathematical geometric shape classification algorithm span various domains, including education, where it enhances interactive learning platforms and provides instant feedback to students.
Aneeza Alam mail , Ali Raza mail , Nisrean Thalji mail , Laith Abualigah mail , Helena Garay mail helena.garay@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Imran Ashraf mail ,
Alam