A formação docente e suas implicações no desenvolvimento de habilidades de leitura e de escrita dos alunos do 2º ano do Ensino Fundamental da Escola Luis Gonzaga Lopes, Guaraciaba do Norte
Tesis Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster Cerrado Portugués Este trabalho, versa sobre uma pesquisa realizada, no período atípico causado pela Pandemia da COVID-19, onde as aulas foram ministradas de forma remota, utilizando-se das Tecnologias da Comunicação e da Informação - TIC, cujos resultados obtidos pelos alunos do 2º ano do Ensino Fundamental da Escola Luís Gonzaga Lopes, Guaraciaba do Norte, no ano de 2020 foram insatisfatórios, no que tange à consolidação da alfabetização. O objetivo geral foi investigar sobre os impactos da formação docente no desenvolvimento das habilidades e competências básicas de leitura e de escrita dos alunos do 2º ano do ensino fundamental dessa escola, no sentido de que estes sejam aprovados para a série seguinte, com as habilidades e competências atinentes à série em curso, consoante a Base Nacional Comum Curricular – BNCC. Utilizou-se como aporte teórico, a BNCC - Brasil (2017); Oliveira (2008); Ceará (2007); Imbérnom (2011); Libâneo (2012), dentre outros. A amostra foi constituída por 25 alunos, sendo 13 do sexo masculino e 14 do sexo feminino, na faixa etária de 7 anos de idade. A metodologia de pesquisa caracteriza-se como uma pesquisa mista, com estudo de caso com abordagem quantitativa e qualitativa, utilizando-se de entrevista fechada e análise documental (fichas e testes aplicados aos alunos). Os principais resultados e conclusões mostraram que mesmo em um período atípico - Pandemia da Covid-19, já no ano subsequente, 2021, onde as aulas continuaram de forma remota, pela internet, o professor, por ter formação acadêmica na área de Língua Portuguesa e também por trabalhar há anos na área da alfabetização e ser capacitado pelo Programa Alfa e Beto - cujo método de ensino é o fônico, bem como ter lecionado na respectiva turma por dois anos consecutivos, ser dedicada e responsável, onde zela pela aprendizagem dos discentes, ter contado com o apoio dos pais, do núcleo gestor da escola, no que tange às orientações aos familiares e responsáveis e a disponibilidade de recursos para trabalhar com as crianças, da equipe técnica da Secretaria de Educação do Município, por meio das formações pedagógicas, do Programa Mais Paic do Governo do Estado do Ceará, permitiram que as crianças alcançaram níveis satisfatórios de aprendizagem em leitura e escrita, embora não se tenha atingindo 100% em todos os quesitos avaliados. Por fim, sabe-se, portanto, que a formação docente, é fundamental, para que os alunos logrem êxito na aprendizagem, pois através dos conhecimentos adquiridos, o professor poderá fazer intervenções pedagógicas eficazes, na zona de desenvolvimento proximal, essenciais ao processo de alfabetização, tendo em vista à aquisição da leitura e da escrita de forma significativa e satisfatória. Palavras-chave: Formação Docente, Alfabetização, Leitura, Escrita metadata Matos Soares, Antonia Márcia mail fernandocr7gba@gmail.com (1978) A formação docente e suas implicações no desenvolvimento de habilidades de leitura e de escrita dos alunos do 2º ano do Ensino Fundamental da Escola Luis Gonzaga Lopes, Guaraciaba do Norte. Masters thesis, Universidad Internacional Iberoamericana Puerto Rico.
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Este trabalho, versa sobre uma pesquisa realizada, no período atípico causado pela Pandemia da COVID-19, onde as aulas foram ministradas de forma remota, utilizando-se das Tecnologias da Comunicação e da Informação - TIC, cujos resultados obtidos pelos alunos do 2º ano do Ensino Fundamental da Escola Luís Gonzaga Lopes, Guaraciaba do Norte, no ano de 2020 foram insatisfatórios, no que tange à consolidação da alfabetização. O objetivo geral foi investigar sobre os impactos da formação docente no desenvolvimento das habilidades e competências básicas de leitura e de escrita dos alunos do 2º ano do ensino fundamental dessa escola, no sentido de que estes sejam aprovados para a série seguinte, com as habilidades e competências atinentes à série em curso, consoante a Base Nacional Comum Curricular – BNCC. Utilizou-se como aporte teórico, a BNCC - Brasil (2017); Oliveira (2008); Ceará (2007); Imbérnom (2011); Libâneo (2012), dentre outros. A amostra foi constituída por 25 alunos, sendo 13 do sexo masculino e 14 do sexo feminino, na faixa etária de 7 anos de idade. A metodologia de pesquisa caracteriza-se como uma pesquisa mista, com estudo de caso com abordagem quantitativa e qualitativa, utilizando-se de entrevista fechada e análise documental (fichas e testes aplicados aos alunos). Os principais resultados e conclusões mostraram que mesmo em um período atípico - Pandemia da Covid-19, já no ano subsequente, 2021, onde as aulas continuaram de forma remota, pela internet, o professor, por ter formação acadêmica na área de Língua Portuguesa e também por trabalhar há anos na área da alfabetização e ser capacitado pelo Programa Alfa e Beto - cujo método de ensino é o fônico, bem como ter lecionado na respectiva turma por dois anos consecutivos, ser dedicada e responsável, onde zela pela aprendizagem dos discentes, ter contado com o apoio dos pais, do núcleo gestor da escola, no que tange às orientações aos familiares e responsáveis e a disponibilidade de recursos para trabalhar com as crianças, da equipe técnica da Secretaria de Educação do Município, por meio das formações pedagógicas, do Programa Mais Paic do Governo do Estado do Ceará, permitiram que as crianças alcançaram níveis satisfatórios de aprendizagem em leitura e escrita, embora não se tenha atingindo 100% em todos os quesitos avaliados. Por fim, sabe-se, portanto, que a formação docente, é fundamental, para que os alunos logrem êxito na aprendizagem, pois através dos conhecimentos adquiridos, o professor poderá fazer intervenções pedagógicas eficazes, na zona de desenvolvimento proximal, essenciais ao processo de alfabetização, tendo em vista à aquisição da leitura e da escrita de forma significativa e satisfatória. Palavras-chave: Formação Docente, Alfabetização, Leitura, Escrita
| Tipo de Documento: | Tesis (Masters) |
|---|---|
| Palabras Clave: | Formação Docente, Alfabetização, Leitura, Escrita |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana Puerto Rico > Docencia > Trabajos finales de Máster |
| Depositado: | 04 Dic 2023 23:30 |
| Ultima Modificación: | 04 Dic 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/2274 |
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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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Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model
Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, a hybrid feature engineering approach is proposed that combines term frequency-inverse document frequency and the bag of words. For experimental evaluation, “Suicide Watch” and “Depression” subreddits on the Reddit platform are used. Results indicate that the proposed SVEM model achieves an accuracy of 94%, better than existing approaches. The model also shows robust performance concerning precision, recall, and F1, each with a 0.93 score. ERT and deep learning models are also used, and performance comparison with these models indicates better performance of the SVEM model. Gated recurrent unit, long short-term memory, and recurrent neural network have an accuracy of 92% while the convolutional neural network obtains an accuracy of 91%. SVEM’s computational complexity is also low compared to deep learning models. Further, this study highlights the importance of explainability in healthcare applications such as suicidal ideation detection, where the use of LIME provides valuable insights into the contribution of different features. In addition, k-fold cross-validation further validates the performance of the proposed approach.
Erol KINA mail , Jin-Ghoo Choi mail , Abid Ishaq mail , Rahman Shafique mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,
KINA
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Human metapneumovirus (hMPV) is one of the potential pandemic pathogens, and it is a concern for elderly subjects and immunocompromised patients. There is no vaccine or specific antiviral available for hMPV. We conducted an in-silico study to predict initial antiviral candidates against human metapneumovirus. Our methodology included protein modeling, stability assessment, molecular docking, molecular simulation, analysis of non-covalent interactions, bioavailability, carcinogenicity, and pharmacokinetic profiling. We pinpointed four plant-derived bio-compounds as antiviral candidates. Among the compounds, apigenin showed the highest binding affinity, with values of − 8.0 kcal/mol for the hMPV-F protein and − 7.6 kcal/mol for the hMPV-N protein. Molecular dynamic simulations and further analyses confirmed that the protein-ligand docked complexes exhibited acceptable stability compared to two standard antiviral drugs. Additionally, these four compounds yielded satisfactory outcomes in bioavailability, drug-likeness, and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) and STopTox analyses. This study highlights the potential of apigenin and xanthoangelol E as an initial antiviral candidate, underscoring the necessity for wet-lab evaluation, preclinical and clinical trials against human metapneumovirus infection.
Hasan Huzayfa Rahaman mail , Afsana Khan mail , Nadim Sharif mail , Wasifuddin Ahmed mail , Nazmul Sharif mail , Rista Majumder mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Isabel De la Torre Díez mail , Shuvra Kanti Dey mail ,
Rahaman
<a href="/17880/1/nutrients-17-03613.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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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.
Marcela Rodríguez- Jiménez mail , Gustavo Daniel Martín-del-Campo-Becerra mail , Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es, Jorge Crespo-Álvarez mail jorge.crespo@uneatlantico.es, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es,
Rodríguez- Jiménez
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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.
Muhammad Mohsin mail , Muhammad Shadab Alam Hashmi mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Helena Garay mail helena.garay@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,
Mohsin