ABSTRACT
Introducción: la información a incluir en el hiperentorno de aprendizaje con contenidos histológicos, y la funcionalidad de este para el trabajo independiente del estudiante, deben ser concebidas integradamente, en una comunión de intereses donde el programa de la asignatura aporte el flujograma pedagógico. Objetivo: diseñar un hiperentorno de aprendizaje que contribuya al desarrollo del trabajo independiente del estudiante, con los contenidos histológicos del proceso de enseñanza-aprendizaje de la asignatura Célula, Tejidos, Sistema Tegumentario, de la carrera de Medicina, en la Universidad de Ciencias Médicas de Matanzas. Materiales y métodos: como método rector se empleó la dialéctica materialista de la filosofía marxista-leninista. Métodos teóricos: histórico-lógico, analítico-documental, inductivo-deductivo, sistémico estructural funcional, modelación. Métodos empíricos: encuesta a estudiantes, entrevista a profesores, observación a clases, selección de expertos. La población estuvo conformada por 10 profesores de la asignatura y una muestra de 290 estudiantes. El trabajo realizado abarcó dos etapas y tres fases: diagnóstico, diseño y valoración de los resultados. Resultados: con relación a requisitos de diseño, la dimensión mejor evaluada fue la usabilidad (4,98). En lo referido a habilidades cognitivas, se destaca, con buenos resultados, el indicador desarrollo de habilidades de navegación en la búsqueda de información, con un valor de 9,30. Conclusiones: el hiperentorno de aprendizaje diseñado es factible, muestra facilidad de uso y funcionalidad al dar respuesta a las necesidades de los estudiantes en la asignatura; se adapta a las posibilidades del estudiante.
Introduction: the information to be included in the learning hyper-environment with histological contents, and its functionality for the student's independent work, must be conceived in an integrative way, in communion of interests where the program of the subject contributes the pedagogical flowchart. Objective: to design a learning hyper-environment that contributes to the development of the student's independent work, with the histological contents of the teaching-learning process of the subject Cell, Tissues, Integumentary System, of the Medicine degree, at the Matanzas University of Medical Sciences of Matanzas. Materials and methods: the materialist dialectics of the Marxist-Leninist philosophy was used as the guiding method. Theoretical methods: historical-logical, analytical-documentary, inductive-deductive, systemic structural functional, modeling. Empirical methods: students survey, teachers interview, observation of classes, selection of experts. The population consisted of 10 teachers of the subject and a sample of 250 students. The work carried out covered two stages and three phases: diagnosis, design and evaluation of the results. Results: regarding design requirements, the best evaluated dimension was usability (4.98). In terms of cognitive skills, the development of navigation skills indicator in the search for information stands out with good results, and a value of 9.30. Conclusions: the designed learning hyper-environment is feasible, shows ease of use and functionality by responding to the needs of students in the subject; it adapts to the possibilities of the student.
ABSTRACT
Exploring the vast compositional space offered by multicomponent systems or high entropy materials using the traditional route of materials discovery, one experiment at a time, is prohibitive in terms of cost and required time. Consequently, the development of high-throughput experimental methods, aided by machine learning and theoretical predictions will facilitate the search for multicomponent materials in their compositional variety. In this study, high entropy oxides are fabricated and characterized using automated high-throughput techniques. For intuitive visualization, a graphical phase-property diagram correlating the crystal structure, the chemical composition, and the band gap are introduced. Interpretable machine learning models are trained for automated data analysis and to speed up data comprehension. The establishment of materials libraries of multicomponent systems correlated with their properties (as in the present work), together with machine learning-based data analysis and theoretical approaches are opening pathways toward virtual development of novel materials for both functional and structural applications.