Evaluación de la investigación educativa con enfoque andragógico
Tesis Materias > Educación Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales Cerrado Español El aporte principal de esta tesis doctoral fue realizar la evaluación de la investigación educativa y diseñar orientaciones metodológicas innovadoras que incluya experiencias docentes, sistematizadas, analizadas y evaluadas en las instituciones de educación superior en beneficio de la investigación educativa desde el enfoque andragógico, ubica las acciones metodológicas y el trabajo docente como insumos para evaluar la investigación educativa de las instituciones de educación superior ubicadas en la zona central de El Salvador. La investigación se realizó de forma no experimental cuantitativa, descriptiva, en el contexto universitario desde el enfoque andragógico es fundamental e indivisible con la investigación educativa, se plantean hipótesis enfocadas al problema y a los objetivos, existe una población docente de 7,880 y se trabajará con una muestra a conveniencia, utilizando la factibilidad de acceso a los docentes de la zona central de 269 docentes, ajustada y estratificada para el proceso de análisis, la información obtenida fue analizada descriptivamente, determinando frecuencias, relaciones de género con respecto a las variables en estudio a Intervalo de Confianza de 95%. Así también se aplicó la prueba de independencia Chi Cuadrado. En todos los análisis se consideró la existencia de diferencias estadísticas significativas para el valor de p<0.05. El resultado proyectado de la investigación fue evaluar la investigación educativa de la zona central de El Salvador y a partir de los hallazgos diseñar orientaciones metodológicas para docentes desde el enfoque andragógico que incorpore acciones de aprendizaje en beneficio de la investigación educativa. En la variedad de enfoques es necesario, la perspectiva andragógica para innovar la investigación educativa, aportando a la formación del docente universitario que desarrolla materias con énfasis en investigación, realizando procesos pertinentes a los perfiles del estudiantado universitario, criticidad, comprensión y autorreflexión como referente del desarrollo complejo en estas etapas y en los niveles de mayor exigencia de aprendizajes significativos. metadata Herrera de Abrego, Santos Nohemy mail nabrego4@yahoo.com (2022) Evaluación de la investigación educativa con enfoque andragógico. Doctoral thesis, SIN ESPECIFICAR.
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El aporte principal de esta tesis doctoral fue realizar la evaluación de la investigación educativa y diseñar orientaciones metodológicas innovadoras que incluya experiencias docentes, sistematizadas, analizadas y evaluadas en las instituciones de educación superior en beneficio de la investigación educativa desde el enfoque andragógico, ubica las acciones metodológicas y el trabajo docente como insumos para evaluar la investigación educativa de las instituciones de educación superior ubicadas en la zona central de El Salvador. La investigación se realizó de forma no experimental cuantitativa, descriptiva, en el contexto universitario desde el enfoque andragógico es fundamental e indivisible con la investigación educativa, se plantean hipótesis enfocadas al problema y a los objetivos, existe una población docente de 7,880 y se trabajará con una muestra a conveniencia, utilizando la factibilidad de acceso a los docentes de la zona central de 269 docentes, ajustada y estratificada para el proceso de análisis, la información obtenida fue analizada descriptivamente, determinando frecuencias, relaciones de género con respecto a las variables en estudio a Intervalo de Confianza de 95%. Así también se aplicó la prueba de independencia Chi Cuadrado. En todos los análisis se consideró la existencia de diferencias estadísticas significativas para el valor de p<0.05. El resultado proyectado de la investigación fue evaluar la investigación educativa de la zona central de El Salvador y a partir de los hallazgos diseñar orientaciones metodológicas para docentes desde el enfoque andragógico que incorpore acciones de aprendizaje en beneficio de la investigación educativa. En la variedad de enfoques es necesario, la perspectiva andragógica para innovar la investigación educativa, aportando a la formación del docente universitario que desarrolla materias con énfasis en investigación, realizando procesos pertinentes a los perfiles del estudiantado universitario, criticidad, comprensión y autorreflexión como referente del desarrollo complejo en estas etapas y en los niveles de mayor exigencia de aprendizajes significativos.
| Tipo de Documento: | Tesis (Doctoral) |
|---|---|
| Palabras Clave: | Evaluación, investigación, andragogía, investigación educativa y educación superior |
| Clasificación temática: | Materias > Educación |
| Divisiones: | Universidad Internacional Iberoamericana México > Investigación > Tesis Doctorales |
| Depositado: | 21 Sep 2023 23:30 |
| Ultima Modificación: | 21 Sep 2023 23:30 |
| URI: | https://repositorio.unini.edu.mx/id/eprint/793 |
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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.
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 ,
Butt
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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.
Premkumar Chithaluru mail , B. Veera Jyothi mail , Fahd S. Alharithi mail , Wojciech Ksiazek mail , M. Ramchander mail , Aman Singh mail aman.singh@uneatlantico.es, Ravi Kumar Rachavaram mail ,
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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,
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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 ,
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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.
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