Alfabetização e Letramento: Como ressignificar a prática docente para o ensino remoto e/ou híbrido
Tesis Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster Cerrado Portugués A presente pesquisa procurou explorar e compreender como vem se dando os processos de ensino e aprendizagem dos estudantes de 1º e 2º Ano da Escola Municipal João Batista de Melo, no município de São Jorge do Patrocínio-PR, durante o período de pandemia, causado pelo Covid-19. O foco da investigação é a prática docente no contexto do ensino remoto/híbrido, nas turmas de alfabetização. Objetivou implementar um programa de formação para os professores alfabetizadores, a fim de potencializar as metodologias e as estratégias utilizadas no processo de alfabetização e letramento, em formato remoto ou híbrido. Os enfoques teóricos mais relevantes pautaram-se em estudos bibliográfico que aborda a concepção dos processos de construção da Linguagem e do Conhecimento segundo Vygotsky e Piaget, na concepção do processo de construção da escrita na perspectiva de Emília Ferreiro e Ana Teberosky, nos estudos dos processos de sistematização da escrita e da sua função social com o domínio do código, de acordo com os estudos de Magda Soares, na concepção de avaliação da aprendizagem na perspectiva diagnóstica, formativa e mediadora seguindo os estudos de Jussara Hoffman e na integração didática por meio de tecnologias digitais na visão de José Manuel Moran Costas, entre outros. A estrutura metodológica da pesquisa apresentou quatro categorias de análise, entre elas, a entrevista exploratória do contexto, o questionário semiestruturado aos docentes, a avaliação diagnóstica dos estudantes e o programa de formação e intervenção pedagógica para estudos teóricos e práticos. Os resultados das análises demonstraram que a referida instituição possui boas estruturas tecnológicas como distribuição de internet cabeada, notebooks em todas as salas de aula, além do pacote de serviços do G suite for Education, que consiste em um conjunto de aplicativos baseados no sistema de nuvem possibilitando aulas disponíveis no classroom, encontros virtuais, aulas e reuniões pelo meet; realização de atividades online, entre outras ferramentas. A análise do questionário mostrou as dificuldades dos professores em se adaptar ao ensino remoto e os desafios para alfabetizar, revela um quadro complexo do ensino que vai desde a insuficiência de internet, de ferramentas tecnológicas, apoio da família e a pouca eficácia do ensino remoto. O resultado da avaliação diagnóstica, mostrou que os estudantes ainda não se apropriaram totalmente do sistema formal da escrita, confirmando o atraso no processo de alfabetização. Já o projeto de intervenção apresentou as possibilidades de utilização dos Recursos Educacionais Digitais e de metodologias ativas e conclui-se que a implementação das Tecnologias da Informação e Comunicação na prática pedagógica se dará de forma gradual e que levará certo tempo para que haja a efetiva incorporação, assimilação e a adaptação do novo modelo didático de ensinar A pesquisa concluiu que, apesar de todas as estratégias utilizadas para garantir o direito de aprendizagem dos estudantes no ensino remoto/ híbrido, fica explícito o prejuízo ou o atraso na apropriação da leitura/escrita, o que impõe aos gestores escolares, um replanejamento minucioso, no que se refere a uma ressignificação do currículo, à avaliação e a organização do trabalho docente. metadata Galiotti de Freitas, Rosangela mail rgaliotti@hotmail.com (2022) Alfabetização e Letramento: Como ressignificar a prática docente para o ensino remoto e/ou híbrido. Masters thesis, SIN ESPECIFICAR.
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A presente pesquisa procurou explorar e compreender como vem se dando os processos de ensino e aprendizagem dos estudantes de 1º e 2º Ano da Escola Municipal João Batista de Melo, no município de São Jorge do Patrocínio-PR, durante o período de pandemia, causado pelo Covid-19. O foco da investigação é a prática docente no contexto do ensino remoto/híbrido, nas turmas de alfabetização. Objetivou implementar um programa de formação para os professores alfabetizadores, a fim de potencializar as metodologias e as estratégias utilizadas no processo de alfabetização e letramento, em formato remoto ou híbrido. Os enfoques teóricos mais relevantes pautaram-se em estudos bibliográfico que aborda a concepção dos processos de construção da Linguagem e do Conhecimento segundo Vygotsky e Piaget, na concepção do processo de construção da escrita na perspectiva de Emília Ferreiro e Ana Teberosky, nos estudos dos processos de sistematização da escrita e da sua função social com o domínio do código, de acordo com os estudos de Magda Soares, na concepção de avaliação da aprendizagem na perspectiva diagnóstica, formativa e mediadora seguindo os estudos de Jussara Hoffman e na integração didática por meio de tecnologias digitais na visão de José Manuel Moran Costas, entre outros. A estrutura metodológica da pesquisa apresentou quatro categorias de análise, entre elas, a entrevista exploratória do contexto, o questionário semiestruturado aos docentes, a avaliação diagnóstica dos estudantes e o programa de formação e intervenção pedagógica para estudos teóricos e práticos. Os resultados das análises demonstraram que a referida instituição possui boas estruturas tecnológicas como distribuição de internet cabeada, notebooks em todas as salas de aula, além do pacote de serviços do G suite for Education, que consiste em um conjunto de aplicativos baseados no sistema de nuvem possibilitando aulas disponíveis no classroom, encontros virtuais, aulas e reuniões pelo meet; realização de atividades online, entre outras ferramentas. A análise do questionário mostrou as dificuldades dos professores em se adaptar ao ensino remoto e os desafios para alfabetizar, revela um quadro complexo do ensino que vai desde a insuficiência de internet, de ferramentas tecnológicas, apoio da família e a pouca eficácia do ensino remoto. O resultado da avaliação diagnóstica, mostrou que os estudantes ainda não se apropriaram totalmente do sistema formal da escrita, confirmando o atraso no processo de alfabetização. Já o projeto de intervenção apresentou as possibilidades de utilização dos Recursos Educacionais Digitais e de metodologias ativas e conclui-se que a implementação das Tecnologias da Informação e Comunicação na prática pedagógica se dará de forma gradual e que levará certo tempo para que haja a efetiva incorporação, assimilação e a adaptação do novo modelo didático de ensinar A pesquisa concluiu que, apesar de todas as estratégias utilizadas para garantir o direito de aprendizagem dos estudantes no ensino remoto/ híbrido, fica explícito o prejuízo ou o atraso na apropriação da leitura/escrita, o que impõe aos gestores escolares, um replanejamento minucioso, no que se refere a uma ressignificação do currículo, à avaliação e a organização do trabalho docente.
| Tipo de Documento: | Tesis (Masters) |
|---|---|
| Palabras Clave: | Alfabetização, Pandemia, Ensino Híbrido, Tecnologias. |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster |
| Depositado: | 03 Nov 2023 23:30 |
| Ultima Modificación: | 03 Nov 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/1845 |
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Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.
Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,
Elío Pascual
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Background/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitations across heterogeneous sources. This study assessed ChatGPT-5, a general-purpose vision-language model, across four scenarios differing in the amount and type of contextual information provided, using a composite dataset to quantify accuracy for calories and macronutrients. Methods: A total of 195 dishes were evaluated, sourced from Allrecipes.com, the SNAPMe dataset, and Home-prepared, weighed meals. Each dish was evaluated under Case 1 (image only), Case 2 (image plus standardized non-visual descriptors), Case 3 (image plus ingredient lists with amounts), and Case 4 (replicates Case 3 but excluding the image). The primary endpoint was kcal Mean Absolute Error (MAE); secondary endpoints included Median Absolute Error (MedAE) and Root Mean Square Error (RMSE) for kcal and macronutrients (protein, carbohydrates, and lipids), all reported with 95% Confidence Intervals (CIs) via dish-level bootstrap resampling and accompanied by absolute differences (Δ) between scenarios. Inference settings were standardized to support reproducibility and variance estimation. Source stratified analyses and quartile summaries were conducted to examine heterogeneity by curation level and nutrient ranges, with additional robustness checks for error complexity relationships. Results and Discussion: Accuracy improved from Case 1 to Case 2 and further in Case 3 for energy and all macronutrients when summarized by MAE, MedAE, and RMSE with 95% CIs, with absolute reductions (Δ) indicating material gains as contextual information increased. In contrast to Case 3, estimation accuracy declined in Case 4, underscoring the contribution of visual cues. Gains were largest in the Home-prepared dietitian-weighed subset and smaller yet consistent for Allrecipes.com and SNAPMe, reflecting differences in reference curation and measurement fidelity across sources. Scenario-level trends were concordant across sources, and stratified and quartile analyses showed coherent patterns of decreasing absolute errors with the provision of structured non-visual information and detailed ingredient data. Conclusions: ChatGPT-5 can deliver practically useful calorie and macronutrient estimates from food images, particularly when augmented with standardized nonvisual descriptors and detailed ingredients, as evidenced by reductions in MAE, MedAE, and RMSE with 95% CIs across scenarios. The decline in accuracy observed when the image was omitted, despite providing detailed ingredient information, indicates that visual cues contribute meaningfully to estimation performance and that improvements are not solely attributable to arithmetic from ingredient lists. Finally, to promote generalizability, it is recommended that future studies include repeated evaluations across diverse datasets, ensure public availability of prompts and outputs, and incorporate systematic comparisons with non-artificial-intelligence baselines.
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Mango is one of the most beloved fruits and plays an indispensable role in the agricultural economies of many tropical countries like Pakistan, India, and other Southeast Asian countries. Similar to other fruits, mango cultivation is also threatened by various diseases, including Anthracnose and Red Rust. Although farmers try to mitigate such situations on time, early and accurate detection of mango diseases remains challenging due to multiple factors, such as limited understanding of disease diversity, similarity in symptoms, and frequent misclassification. To avoid such instances, this study proposes a multimodal deep learning framework that leverages both leaf and fruit images to improve classification performance and generalization. Individual CNN-based pre-trained models, including ResNet-50, MobileNetV2, EfficientNet-B0, and ConvNeXt, were trained separately on curated datasets of mango leaf and fruit diseases. A novel Modality Attention Fusion (MAF) mechanism was introduced to dynamically weight and combine predictions from both modalities based on their discriminative strength, as some diseases are more prominent on leaves than on fruits, and vice versa. To address overfitting and improve generalization, a class-aware augmentation pipeline was integrated, which performs augmentation according to the specific characteristics of each class. The proposed attention-based fusion strategy significantly outperformed individual models and static fusion approaches, achieving a test accuracy of 99.08%, an F1 score of 99.03%, and a perfect ROC-AUC of 99.96% using EfficientNet-B0 as the base. To evaluate the model’s real-world applicability, an interactive web application was developed using the Django framework and evaluated through out-of-distribution (OOD) testing on diverse mango samples collected from public sources. These findings underline the importance of combining visual cues from multiple organs of plants and adapting model attention to contextual features for real-world agricultural diagnostics.
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Food security is a universal need worldwide. This study explored the relationship between food security and adherence to the Mediterranean diet in the context of the DELICIOUS project. A survey involving 2,011 parents of children and adolescents aged 6–17 years was conducted. Adherence to the Mediterranean diet was assessed through the KIDMED score. Information regarding the ease of accessing Mediterranean foods, economic allowance, employment and residence was collected. Logistic regressions analyses were performed to test the associations. Individuals living in rural areas and reporting difficulty in obtaining all studied foods were less likely to follow the Mediterranean diet. Higher adherence was associated with a household monthly income higher than €4000. No associations with family status and no differences across countries were found. The progressive shift away from the Mediterranean diet may depend not only on cultural preferences for unhealthier, industrial alternatives but also on family budgets and food accessibility.
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