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This work seeks to support scholars interested in non-face-to-face modalities of higher education in making decisions about the use of digital and educational technologies (DET) to promote educational transformation (ET) in the context of their organizations. This organizational change deals with the implementation of technology-based flexible educational practices, focused on helping students develop competencies of interest for their personal and professional growth. With this in mind, in 2018 we identified and followed six leading higher education institutions on three continents that, for years, have carried out educational innovation experiences with the support of technology. Two of the analyzed experiences make use of eLearning, another of bLearning, and the others combine eLearning and bLearning as a complement to the face-to-face modality. The meta-analysis of the cases, carried out in 2019, followed suggestions from (Stake in The art of case study research, Sage Publications Inc., 1995) as well as from qualitative research that seeks to understand what is behind the cases from three dimensions: education, technology, organization. For each one, we determined what they do, how they do it, and what success factors must be considered. As the data was collected before the 2020 pandemic and this issue produced structural imbalances in society and in higher education, it was considered pertinent, at the end of 2020, to check the pulse of the ET mediated with DET in three of the six institutions studied. The purpose was to refine the findings of the meta-analysis and learn from the decisions made in the situation of forced change in environments, means, and strategies to continue providing quality higher education.

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This study aims to evaluate the effectiveness of an online course to enable orthopaedic surgeons to acquire the core competencies necessary to prevent and treat fracture-related infections (FRI). This study included orthopaedic surgeons and residents from Latin American countries who attended an online course focused on FRI. The online course included: didactic lectures, small-group clinical case discussions, and panel case discussions. The course was delivered using Zoom® platform and designed to address four core competencies: prevention, definition and diagnosis, antimicrobial therapy, and surgical treatment. An online questionnaire was created distributing 16 questions through six clinical scenarios. Participants were invited to answer the questionnaire before and after the course. Sixty of the 78 course participants answered the pre-course, and 42 the post-course assessment. Relative to before the course, the mean post-course assessment score rose significantly for prevention of FRI (4.1 before and 4.5 after; p = 0.014), definition and diagnosis (2.4 before and 3.4 after; p = 0.001), and surgical treatment (2.2 before and 2.8 after; p = 0.011). The final score encompassing all four core competencies also rose significantly (2.7 before and 3.3 after; p = 0.001). The online course on FRI was feasible and effective, significantly increasing course users' knowledge of overall competency in managing FRI.

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Abstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their difficulty using selected data mining methods (classification and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, efficient, statistical trees (QUEST)), and to identify the most significant factors affecting calving difficulty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish Holstein- Friesian black-and-white heifers from four farms were used. Calving difficulty was divided into three categories (easy, moderate and difficult). Results: The percentages of calvings correctly classified by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (difficult), respectively. The most important factors affecting calving difficulty were bull's rank (based on the mean calving difficulty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classification models were satisfactory and could predict the class of calving difficulty.

Resumen Antecedentes: La distocia en el ganado resulta en consecuencias adversas (elevadas morbilidad y mortalidad de terneros, reducida fertilidad y producción de leche, menor supervivencia y bienestar de las vacas) que conllevan a pérdidas económicas considerables. Objetivo: Clasificar los partos del ganado lechero en función de su grado de dificultad a través de métodos seleccionados de minería de datos (árboles de clasificación y de regresión (CART), detección automática de interacción chi-cuadrado (CHAID) y árboles estadísticos no sesgados y eficientes (QUEST)) e identificar los factores más característicos de dificultad al parto. Los resultados de los métodos de minería de datos se compararon con los del modelo lineal generalizado tradicional (GLM). Métodos: Se utilizaron 1.342 registros de parto de novillas de raza polaca Holstein-Friesian blanca y negra de cuatro explotaciones lecheras. La dificultad de parto del ganado se dividió en tres categorías (fácil, moderado y difícil). Resultados: El porcentaje de partos correctamente clasificados por CART, CHAID, QUEST y GLM fue 35,14, 18,92, 19,82 y 43,24% (fácil), 68,70, 73,91, 81,74 y 41,74% (moderado), y 77,27, 85,45, 73,64 y 81,82% (difícil), respectivamente. Los factores más importantes de dificultad de parto fueron el rango de toro (determinado sobre la base de dificultad media de los partos de sus hijas), la edad al parto, la categoría de las fincas (sobre la base del rendimiento medio de leche) y la temporada de parto. Conclusión: Todos los modelos de clasificación se caracterizaron como satisfactorios y podrían predecir la clase de dificultad al parto.

Resumo Antecedentes: A distócia em bovinos resulta em consequências adversas (aumento da morbidade e mortalidade dos bezerros, diminuição da fertilidade e da produção de leite, baixa sobrevivência da vaca e redução do bem-estar) levando a consideráveis perdas econômicas. Objetivo: Classificar os partos do gado leiteiro segundo o seu grau de dificuldade através dos métodos selecionados de data mining (árvores de classificação e regressão (CART), detecção automática de interação chi-quadrado (CHAID) e ârvores estatísticas eficientes e rápidas e imparciais (QUEST)) e identificar os fatores mais importantes para a dificuldade nos partos. Os resultados dos métodos de data mining foram comparados com os resultados do modelo lineal generalizado (GLM) mais convencional. Métodos: Foram utilizados 1.342 registos de partos de novilhas da raça polaca Holstein-Frísia branca e preta de quatro fazendas. A dificuldade em um parto foi dividida em três categorias (fácil, média, difícil). Resultados: A percentagem de partos corretamente classificados através de CART, CHAID, QUEST e GLM foram de 35,14, 18,92, 19,82 e 43,24% (fácil), 68,70, 73,91, 81,74 e 41,74% (média) e 77,27, 85,45, 73,64 e 81,82% (difícil), respetivamente. Os fatores mais importantes de dificuldade no parto foram a classificação do touro (determinada com base na dificuldade média nos partos de suas filhas), a idade no momento de parto, a categoria de exploração leiteira (com base no rendimento médio de leite) e a temporada de parto. Conclusão: Todos os modelos de classificação destacaram-se por sua qualidade satisfatória e foram capazes de prever a categoria de dificuldade de um parto.