Liderazgo Pedagógico y Gestión Escolar del Directivo: Un análisis al aporte de la Calidad Educativa.

Tesis Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Español Las instituciones educativas constituyen el escenario propicio para generar cambios sustanciales en la sociedad. Es allí, donde se forman jóvenes que tendrán en sus manos el futuro de los pueblos. En tal sentido, en cada directivo recae gran responsabilidad como rector garante de estos espacios académicos que debe propiciar la calidad educativa como proceso eficaz de cara a las necesidades del contexto. Desde este punto de vista, esta investigación doctoral tuvo como propósito formular líneas estratégicas gerenciales basadas en el liderazgo pedagógico y la gestión escolar del directivo para mejorar la calidad educativa de las instituciones educativas fiscales de la ciudad de Quito. Este trabajo se suscribe a una investigación de corte mixta con una mirada complementaria de los paradigmas positivista e interpretativo crítico, apoyada en un estudio de campo de carácter descriptivo correlacional, inferencial con el coeficiente de correlación de Pearson, un modelo de regresión lineal múltiple o multivariante, así como un análisis hermenéutico del fenómeno social - educativo en el que se insertan las instituciones educativas. A tal efecto la muestra estuvo constituida por 217 directivos de los Distritos Educativos y por 374 docentes pertenecientes a la Coordinación Zonal de Educación No. 9 dentro de la división administrativa del Ministerio de Educación del Ecuador. Los sujetos se seleccionaron a través de un muestreo por conglomerado. La técnica para la recolección de información es la encuesta a través de un cuestionario autoadministrado contentivo de las dimensiones e indicadores de las variables en estudio. Los resultados del estudio determinaron la correlación significativa positiva entre diferentes dimensiones de las variables, así como la incidencia de las variables independientes gestión escolar y liderazgo pedagógico, sobre la variable dependiente calidad educativa. De allí, que se concluye que las hipótesis formuladas son correctas y, en consecuencia, se aceptan. Finalmente, se describen las implicaciones y limitaciones, no sin antes presentar una propuesta de líneas estratégicas gerenciales para abordar los hallazgos encontrados en la realidad estudiada. metadata Barba Miranda, Laura Cristina mail laubami1@gmail.com (2021) Liderazgo Pedagógico y Gestión Escolar del Directivo: Un análisis al aporte de la Calidad Educativa. Doctoral thesis, Universidad Internacional Iberoamericana México.

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Las instituciones educativas constituyen el escenario propicio para generar cambios sustanciales en la sociedad. Es allí, donde se forman jóvenes que tendrán en sus manos el futuro de los pueblos. En tal sentido, en cada directivo recae gran responsabilidad como rector garante de estos espacios académicos que debe propiciar la calidad educativa como proceso eficaz de cara a las necesidades del contexto. Desde este punto de vista, esta investigación doctoral tuvo como propósito formular líneas estratégicas gerenciales basadas en el liderazgo pedagógico y la gestión escolar del directivo para mejorar la calidad educativa de las instituciones educativas fiscales de la ciudad de Quito. Este trabajo se suscribe a una investigación de corte mixta con una mirada complementaria de los paradigmas positivista e interpretativo crítico, apoyada en un estudio de campo de carácter descriptivo correlacional, inferencial con el coeficiente de correlación de Pearson, un modelo de regresión lineal múltiple o multivariante, así como un análisis hermenéutico del fenómeno social - educativo en el que se insertan las instituciones educativas. A tal efecto la muestra estuvo constituida por 217 directivos de los Distritos Educativos y por 374 docentes pertenecientes a la Coordinación Zonal de Educación No. 9 dentro de la división administrativa del Ministerio de Educación del Ecuador. Los sujetos se seleccionaron a través de un muestreo por conglomerado. La técnica para la recolección de información es la encuesta a través de un cuestionario autoadministrado contentivo de las dimensiones e indicadores de las variables en estudio. Los resultados del estudio determinaron la correlación significativa positiva entre diferentes dimensiones de las variables, así como la incidencia de las variables independientes gestión escolar y liderazgo pedagógico, sobre la variable dependiente calidad educativa. De allí, que se concluye que las hipótesis formuladas son correctas y, en consecuencia, se aceptan. Finalmente, se describen las implicaciones y limitaciones, no sin antes presentar una propuesta de líneas estratégicas gerenciales para abordar los hallazgos encontrados en la realidad estudiada.

Tipo de Documento: Tesis (Doctoral)
Palabras Clave: Liderazgo pedagógico, Calidad educativa, Gestión escolar, Instituciones fiscales, Sistema educativo
Clasificación temática: Materias > Ingeniería
Divisiones: Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales
Depositado: 26 Nov 2021 23:55
Ultima Modificación: 20 Sep 2023 23:30
URI: https://repositorio.unini.edu.mx/id/eprint/444

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Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.

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Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,

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A Systematic Literature Review on Integrated Deep Learning and Multi-Agent Vision-Language Frameworks for Pathology Image Analysis and Report Generation

This systematic literature review (SLR) investigates the integration of deep learning (DL), vision-language models(VLMs), and multi-agent systems in the analysis of pathology images and automated report generation. The rapidadvancement of whole-slide imaging (WSI) technologies has posed new challenges in pathology, especially due to thescale and complexity of the data. DL techniques in general and convolutional neural networks (CNNs) and transform-ers in particular have significantly enhanced image analysis tasks including segmentation, classification, and detection.However, these models often lack generalizability to generate coherent, clinically relevant text, thus necessitating theintegration of VLMs and large language models (LLMs). This review examines the effectiveness of VLMs and LLMsin bridging the gap between visual data and clinical text, focusing on their potential for automating the generationof pathology reports. Additionally, multi-agent systems, which leverage specialized artificial intelligence (AI) agentsto collaboratively perform diagnostic tasks, are explored for their contributions to improving diagnostic accuracy andscalability. Through a synthesis of recent studies, this review highlights the successes, challenges, and future direc-tions of these AI technologies in pathology diagnostics, offering a comprehensive foundation for the development ofintegrated, AI-driven diagnostic workflows.

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Ali

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A scalable and secure federated learning authentication scheme for IoT

Secure and scalable authentication remains a fundamental challenge in Internet of Things (IoT) networks due to constrained device resources, dynamic topology, and the absence of centralized trust infrastructures. Conventional password-based and certificate-driven authentication schemes incur high computation, storage, and communication overhead, limiting their suitability for large-scale deployments. To address these limitations, this paper proposes ScLBS, a federated learning (FL)–based self-certified authentication scheme for distributed and sustainable IoT environments. ScLBS integrates self-certified public key cryptography with FL-driven trust adaptation, enabling decentralized public key derivation without reliance on third-party certificate authorities or exposure of private credentials. A zero-knowledge mechanism combined with location-aware authentication strengthens resistance to impersonation, Sybil, and replay attacks. Hierarchical key management supported by a -tree enables efficient group rekeying and preserves forward and backward secrecy under dynamic membership. Formal security verification is conducted under the Dolev–Yao adversary model using ProVerif, confirming secrecy of private and session keys (SKs) and correctness of authentication. Extensive NS-3 simulations and ablation analysis demonstrate that ScLBS achieves lower authentication delay, reduced message overhead, improved network utilization, and decreased energy consumption compared to representative IoT authentication schemes, while maintaining bounded FL overhead. These results indicate that ScLBS provides a balanced trade-off between security strength, scalability, and resource efficiency for constrained IoT networks.

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Human Activity Recognition in Domestic Settings Based on Optical Techniques and Ensemble Models

Human activity recognition (HAR) is essential in many applications, such as smart homes, assisted living, healthcare monitoring, rehabilitation, physiotherapy, and geriatric care. Conventional methods of HAR use wearable sensors, e.g., acceleration sensors and gyroscopes. However, they are limited by issues such as sensitivity to position, user inconvenience, and potential health risks with long-term use. Optical camera systems that are vision-based provide an alternative that is not intrusive; however, they are susceptible to variations in lighting, intrusions, and privacy issues. The paper uses an optical method of recognizing human domestic activities based on pose estimation and deep learning ensemble models. The skeletal keypoint features proposed in the current methodology are extracted from video data using PoseNet to generate a privacy-preserving representation that captures key motion dynamics without being sensitive to changes in appearance. A total of 30 subjects (15 male and 15 female) were sampled across 2734 activity samples, including nine daily domestic activities. There were six deep learning architectures, namely, the Transformer (Transformer), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), One-Dimensional Convolutional Neural Network (1D CNN), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture. The results on the hold-out test set show that the CNN–LSTM architecture achieves an accuracy of 98.78% within our experimental setting. Leave-One-Subject-Out cross-validation further confirms robust generalization across unseen individuals, with CNN–LSTM achieving a mean accuracy of 97.21% ± 1.84% across 30 subjects. The results demonstrate that vision-based pose estimation with deep learning is a useful, precise, and non-intrusive approach to HAR in smart healthcare and home automation systems.

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Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students

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.

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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,

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