Inclusión Educativa y la Enseñanza Bilingüe en el Nivel de Primaria en un Centro Escolar de Honduras
Tesis Materias > Educación Universidad Internacional Iberoamericana Puerto Rico > Investigación > Tesis Doctorales Cerrado Español La investigación analiza la implementación de un modelo multinivel inclusivo utilizado para la enseñanza del inglés como segunda lengua. Se estudia el modelo MTSS, Sistema de Soporte Multi Nivel, en la clase de lectura en inglés y el rendimiento académico de estudiantes sin y con algún tipo de Necesidad Educativa Especial (NEE) dentro del sistema de educación bilingüe. Se diseñó y se validó un Plan Integral Educativo (PIE) basado en el Index de Inclusión de Ainscow y Booth (2015) y anclado en la estructura multinivel. Específicamente busca examinar la implementación de las prácticas y valores inclusivos en el área de primaria vinculada con la enseñanza bilingüe del inglés como segunda lengua, el aprendizaje y el rendimiento académico. Es un diseño cuantitativo, de modalidad descriptiva y comparativa entre grupo experimental y grupo control. En este, participaron un total de 132 estudiantes del primer grado de primaria de la Escuela Internacional Sampedrana de Honduras, un total de 20 estudiantes con NEE. La recolección de datos se realizó mediante cuestionarios, pruebas académicas específicas de lectura en inglés, observaciones vivenciales y encuesta de satisfacción del grupo experimental. Los datos estadísticos fueron informatizados en los softwares de Excel. Entre los hallazgos, la implementación de PIE y la estructura inclusiva multinivel favorece el rendimiento académico de la lectura en inglés de los estudiantes sin y con NEE. En todas las pruebas académicas los resultados del grupo experimental son superiores al grupo control. El alumnado con algún tipo de NEE logró aprender a leer y comprender en inglés como su segunda lengua. El equipo docente propició el ambiente educativo respetando los ritmos de aprendizaje, tiempo de internalización e implementando acomodaciones curriculares. Las estrategias de apoyo y las prácticas inclusivas dan oportunidades efectivas de aprendizaje de una segunda lengua. metadata Martinez Magaña, Laura Aracely mail lmartinez@seishn.com (2023) Inclusión Educativa y la Enseñanza Bilingüe en el Nivel de Primaria en un Centro Escolar de Honduras. Doctoral thesis, SIN ESPECIFICAR.
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La investigación analiza la implementación de un modelo multinivel inclusivo utilizado para la enseñanza del inglés como segunda lengua. Se estudia el modelo MTSS, Sistema de Soporte Multi Nivel, en la clase de lectura en inglés y el rendimiento académico de estudiantes sin y con algún tipo de Necesidad Educativa Especial (NEE) dentro del sistema de educación bilingüe. Se diseñó y se validó un Plan Integral Educativo (PIE) basado en el Index de Inclusión de Ainscow y Booth (2015) y anclado en la estructura multinivel. Específicamente busca examinar la implementación de las prácticas y valores inclusivos en el área de primaria vinculada con la enseñanza bilingüe del inglés como segunda lengua, el aprendizaje y el rendimiento académico. Es un diseño cuantitativo, de modalidad descriptiva y comparativa entre grupo experimental y grupo control. En este, participaron un total de 132 estudiantes del primer grado de primaria de la Escuela Internacional Sampedrana de Honduras, un total de 20 estudiantes con NEE. La recolección de datos se realizó mediante cuestionarios, pruebas académicas específicas de lectura en inglés, observaciones vivenciales y encuesta de satisfacción del grupo experimental. Los datos estadísticos fueron informatizados en los softwares de Excel. Entre los hallazgos, la implementación de PIE y la estructura inclusiva multinivel favorece el rendimiento académico de la lectura en inglés de los estudiantes sin y con NEE. En todas las pruebas académicas los resultados del grupo experimental son superiores al grupo control. El alumnado con algún tipo de NEE logró aprender a leer y comprender en inglés como su segunda lengua. El equipo docente propició el ambiente educativo respetando los ritmos de aprendizaje, tiempo de internalización e implementando acomodaciones curriculares. Las estrategias de apoyo y las prácticas inclusivas dan oportunidades efectivas de aprendizaje de una segunda lengua.
| Tipo de Documento: | Tesis (Doctoral) |
|---|---|
| Palabras Clave: | NEE, MTSS, Prácticas Inclusivas, Lectura en Inglés, Bilingüismo, Inglés como Segunda Lengua, Equidad Educativa, Necesidades Educativas Especiales en Latinoamérica |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana Puerto Rico > Investigación > Tesis Doctorales |
| Depositado: | 26 Sep 2023 23:30 |
| Ultima Modificación: | 26 Sep 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/5646 |
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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.
Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,
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Novel hybrid transfer neural network for wheat crop growth stages recognition using field images
Wheat is one of the world’s most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential for precision farming. Determining wheat growth stages accurately is crucial for increasing the efficiency of agricultural yield in wheat farming. Preliminary research identified obstacles in distinguishing between these stages, negatively impacting crop yields. To address this, this study introduces an innovative approach, MobDenNet, based on data collection and real-time wheat crop stage recognition. The data collection utilized a diverse image dataset covering seven growth phases ‘Crown Root’, ‘Tillering’, ‘Mid Vegetative’, ‘Booting’, ‘Heading’, ‘Anthesis’, and ‘Milking’, comprising 4496 images. The collected image dataset underwent rigorous preprocessing and advanced data augmentation to refine and minimize biases. This study employed deep and transfer learning models, including MobileNetV2, DenseNet-121, NASNet-Large, InceptionV3, and a convolutional neural network (CNN) for performance comparison. Experimental evaluations demonstrated that the transfer model MobileNetV2 achieved 95% accuracy, DenseNet-121 achieved 94% accuracy, NASNet-Large achieved 76% accuracy, InceptionV3 achieved 74% accuracy, and the CNN achieved 68% accuracy. The proposed novel hybrid approach, MobDenNet, that synergistically merges the architectures of MobileNetV2 and DenseNet-121 neural networks, yields highly accurate results with precision, recall, and an F1 score of 99%. We validated the robustness of the proposed approach using the k-fold cross-validation. The proposed research ensures the detection of growth stages with great promise for boosting agricultural productivity and management practices, empowering farmers to optimize resource distribution and make informed decisions.
Aisha Naseer mail , Madiha Amjad mail , Ali Raza mail , Kashif Munir mail , Aseel Smerat mail , Henry Fabian Gongora mail henry.gongora@uneatlantico.es, Carlos Eduardo Uc Ríos mail carlos.uc@unini.edu.mx, Imran Ashraf mail ,
Naseer
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Client engagement solution for post implementation issues in software industry using blockchain
In the rapidly advanced and evolving information technology industry, adequate client engagement plays a critical role as it is very important to understand the client’s concerns, and requirements, have the records, authorizations, and go-ahead of previously agreed requirements, and provide the feasible solution accordingly. Previously multiple solutions have been proposed to enhance the efficiency of client engagement, but they lack traceability, trust, transparency, and conflict in agreements of previous contracts. Due to the lack of these shortcomings, the client requirement is getting delayed which is causing client escalations, integrity issues, project failure, and penalties. In this study, we proposed the UniferCollab framework to overcome the issues of collaboration between various teams, transparency, the record of client authorizations, and the go-ahead on previous developments by implementing blockchain technology. We store the data on the permissible network in the proposed approach. It allows us to compile all the requirements and information shared by clients on permissible blockchain to secure a large amount of data which enhances the traceability of all the requirements. All the authorizations from the client generate push notifications for any changes in their current system executed through smart contracts. It removes the ambiguity between various development teams if the client has only shared the requirement with one team. The data is stored in the decentralized network from where information is gathered which resolves the traceability, transparency, and trust issues. Lastly, evaluations involved a total of 800 hypertext transfer protocol (HTTP) requests tested using Postman with blockchain block sizes ranging from 0.568 KB to 550 KB and an average size increase of 280 KB was observed as new blocks were added. The longest chain in the network was observed during 800 repetitions of blockchain operations. Latency analysis revealed that delays in processing HTTP requests were influenced by decentralized node processing, local machine response times, and internet bandwidth through various experiments. Results show that the proposed framework resolves all client engagement issues in implementation between all stakeholders which enhances trust, and transparency improves client experience and helps us manage disputes effectively.
Muhammad Shoaib Farooq mail , Khurram Irshad mail , Danish Riaz mail , Nagwan Abdel Samee mail , Ernesto Bautista Thompson mail ernesto.bautista@unini.edu.mx, Daniel Gavilanes Aray mail daniel.gavilanes@uneatlantico.es, Imran Ashraf mail ,
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This Special Issue of Diet and Nutrition: Metabolic Diseases showcases cutting-edge research exploring the intersection between nutrition, dietary patterns, and public health. The contributions in this collection involve both fundamental and applied research, offering new insights into how nutrition can combat the growing global burden of non-communicable diseases [1]. The studies in this issue emphasize the critical role that diet plays in promoting metabolic health, preventing chronic diseases, and improving overall quality of life. In recent years, nutrition has become a central focus in global health efforts, with a growing body of evidence demonstrating its impact on both individual and population-level outcomes [2,3]. This Special Issue encompasses several key themes, including the role of dietary interventions in managing metabolic disorders, the importance of nutrient timing and quality, and the broader implications of sustainable dietary practices.
Iñaki Elío Pascual mail inaki.elio@uneatlantico.es,
Elío Pascual
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Ensemble stacked model for enhanced identification of sentiments from IMDB reviews
The emergence of social media platforms led to the sharing of ideas, thoughts, events, and reviews. The shared views and comments contain people’s sentiments and analysis of these sentiments has emerged as one of the most popular fields of study. Sentiment analysis in the Urdu language is an important research problem similar to other languages, however, it is not investigated very well. On social media platforms like X (Twitter), billions of native Urdu speakers use the Urdu script which makes sentiment analysis in the Urdu language important. In this regard, an ensemble model RRLS is proposed that stacks random forest, recurrent neural network, logistic regression (LR), and support vector machine (SVM). The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. The Urdu hack library was used to preprocess the Urdu data, which includes preprocessing operations including normalizing individual letters, merging them, including spaces, etc. concerning punctuation. The problem of accurately encoding Urdu characters and replacing Arabic letters with their Urdu equivalents is fixed by the normalization module. Several models are adopted in this study for extensive evaluation of their accuracy for Urdu sentiment analysis. While the results promising, among machine learning models, the SVM and LR attained an accuracy of 87%, according to performance criteria such as F-measure, accuracy, recall, and precision. The accuracy of the long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) was 84%. The suggested ensemble RRLS model performs better than other learning algorithms and achieves a 90% accuracy rate, outperforming current methods. The use of the synthetic minority oversampling technique (SMOTE) is observed to improve the performance and lead to 92.77% accuracy.
Komal Azim mail , Alishba Tahir mail , Mobeen Shahroz mail , Hanen Karamti mail , Annia A. Vázquez mail annia.almeyda@uneatlantico.es, Angel Olider Rojas Vistorte mail angel.rojas@uneatlantico.es, Imran Ashraf mail ,
Azim