Rubricas: ferramentas potencializadoras para uma avaliação mediadora em prol de aprendizagens mais significativas
Thesis Subjects > Education Ibero-american International University > Teaching > Master's Final Projects Closed Portuguese Este trabalho, intitulado “Rubricas: ferramentas potencializadoras para uma avaliação mediadora em prol de aprendizagens mais significativas” apresenta o resultado de uma pesquisa realizada com dez professores do Ensino Médio da Escola Estadual Maria Cecília Barbosa Leal, localizada em Surubim – Pernambuco. Teve por objetivo identificar como os docentes utilizam as rubricas no processo de avaliação dos estudantes do Ensino Médio, para garantir uma aprendizagem significativa, à luz das definições de rubricas de Lüdke, Stevens, Levi, Andrade e Popham; da classificação e características das rubricas na visão de Porto; da concepção de avaliação mediadora de Hoffmann, Piaget e Vygotsky; e da visão de Ausubel e Moreira sobre aprendizagem significativa. Ainda fundamentou o trabalho a avaliação de habilidades na visão de Stevens e Levi; práticas inovadoras de avaliação em metodologias ativas de aprendizagem encharcadas de vantagens, pois possibilitam a autonomia do aluno e maior desenvolvimento do senso crítico, senso de responsabilidade, maior interação e colaboração entre professor e aluno, aluno e alunos; as tecnologias como ferramentas que potencializam o processo avaliativo; as metodologias ativas como estratégia de envolver os estudantes e torná-los protagonistas de sua aprendizagem.Trata-se de uma pesquisa de abordagem qualitativa na visão de Minayo, abordando o que vai além das relações e dos processos e considerando relevantes os inúmeros significados, desejos, culturas, valores e atitudes para obter uma visão mais ampla do contexto. A pesquisa qualitativa é realizada sem a preocupação em representar através de números, isto é, quantificá-la. Porém, com foco na compreensão da dinâmica de um grupo de educadores para buscar e analisar os discursos, os comportamentos sobre o processo avaliativo que estão inseridos. Como metodologia utiliza-se a pesquisa-ação, visando à promoção de condições para ações e transformações de situações dentro da própria escola. Pesquisa, baseada na concepção de Gil, realizada por meio de entrevistas (onze perguntas) e observação do comportamento com dez educadores do Ensino Médio. Através da análise dos dados e resultado da investigação, as evidências apontaram que poucos professores conhecem e compreendem o conceito de rubricas, e os que conhecem e compreendem, pouco as utilizam para favorecer a avaliação formativa e garantir uma aprendizagem significativa em prol do protagonismo dos estudantes. Nesse sentido, ratifica-se que é preciso que os professores desconstruam algumas concepções e ressignifiquem algumas práticas para tornar o processo avaliativo mais justo, encantar e envolver seus alunos durante todo o processo educativo, formando cidadãos protagonistas e críticos. metadata de Farias Pessoa Santana, Eklaydja mail eklaydja.farias@bol.com.br (2022) Rubricas: ferramentas potencializadoras para uma avaliação mediadora em prol de aprendizagens mais significativas. Master's thesis, UNSPECIFIED.
Full text not available.Abstract
Este trabalho, intitulado “Rubricas: ferramentas potencializadoras para uma avaliação mediadora em prol de aprendizagens mais significativas” apresenta o resultado de uma pesquisa realizada com dez professores do Ensino Médio da Escola Estadual Maria Cecília Barbosa Leal, localizada em Surubim – Pernambuco. Teve por objetivo identificar como os docentes utilizam as rubricas no processo de avaliação dos estudantes do Ensino Médio, para garantir uma aprendizagem significativa, à luz das definições de rubricas de Lüdke, Stevens, Levi, Andrade e Popham; da classificação e características das rubricas na visão de Porto; da concepção de avaliação mediadora de Hoffmann, Piaget e Vygotsky; e da visão de Ausubel e Moreira sobre aprendizagem significativa. Ainda fundamentou o trabalho a avaliação de habilidades na visão de Stevens e Levi; práticas inovadoras de avaliação em metodologias ativas de aprendizagem encharcadas de vantagens, pois possibilitam a autonomia do aluno e maior desenvolvimento do senso crítico, senso de responsabilidade, maior interação e colaboração entre professor e aluno, aluno e alunos; as tecnologias como ferramentas que potencializam o processo avaliativo; as metodologias ativas como estratégia de envolver os estudantes e torná-los protagonistas de sua aprendizagem.Trata-se de uma pesquisa de abordagem qualitativa na visão de Minayo, abordando o que vai além das relações e dos processos e considerando relevantes os inúmeros significados, desejos, culturas, valores e atitudes para obter uma visão mais ampla do contexto. A pesquisa qualitativa é realizada sem a preocupação em representar através de números, isto é, quantificá-la. Porém, com foco na compreensão da dinâmica de um grupo de educadores para buscar e analisar os discursos, os comportamentos sobre o processo avaliativo que estão inseridos. Como metodologia utiliza-se a pesquisa-ação, visando à promoção de condições para ações e transformações de situações dentro da própria escola. Pesquisa, baseada na concepção de Gil, realizada por meio de entrevistas (onze perguntas) e observação do comportamento com dez educadores do Ensino Médio. Através da análise dos dados e resultado da investigação, as evidências apontaram que poucos professores conhecem e compreendem o conceito de rubricas, e os que conhecem e compreendem, pouco as utilizam para favorecer a avaliação formativa e garantir uma aprendizagem significativa em prol do protagonismo dos estudantes. Nesse sentido, ratifica-se que é preciso que os professores desconstruam algumas concepções e ressignifiquem algumas práticas para tornar o processo avaliativo mais justo, encantar e envolver seus alunos durante todo o processo educativo, formando cidadãos protagonistas e críticos.
| Document Type: | Thesis (Master's) |
|---|---|
| Keywords: | Rubricas, Avaliação formativa, Aprendizagem significativa, Protagonismo dos estudantes. |
| Subject classification: | Subjects > Education |
| Divisions: | Ibero-american International University > Teaching > Master's Final Projects |
| Deposited: | 07 Dec 2023 23:30 |
| Last Modified: | 07 Dec 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/2427 |
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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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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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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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Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model
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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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