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      Análisis multivariante del uso de espacios virtualizados por estudiantes pregraduados en Ciencias de la Salud Translated title: Multivariate analysis of the use of virtualized spaces by Health Sciences undergraduate students

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          Abstract

          Introducción. Los entornos virtuales de aprendizaje permiten crear espacios dinámicos y facilitadores del aprendizaje. Investigar el uso dado por los estudiantes puede identificar patrones de comportamiento y detectar tempranamente alumnos en riesgo de abandono, y se han descrito correlaciones entre su uso y el rendimiento académico. Materiales y métodos. Se estudiaron siete espacios virtualizados correspondientes a cuatro asignaturas de tres grados de Ciencias de la Salud impartidas en los cursos 2017/2018 y 2018/2019, con un total de 517 estudiantes. Previamente se extrajeron, depuraron y anonimizaron los registros de cada espacio. Las variables analizadas fueron: número de visitas al campus virtual, de accesos a recursos y a URL, y uso del foro. Se aplicó un análisis de correspondencias múltiples, seguido de un análisis de conglomerados jerárquico. Resultados. Se obtuvieron cuatro clústeres, con tamaños de entre el 20,9 y el 29,4% de los estudiantes, caracterizados por comportamientos diferenciales en cuanto al uso del campus virtual, y se establecieron relaciones con las calificaciones finales, las notas teóricas y las prácticas de las asignaturas. Se observa que, a menor interacción en el campus virtual, menor rendimiento académico, mientras que, a mayor actividad registrada, mejores calificaciones. Conclusiones. Nuestro estudio revela grupos de estudiantes con comportamientos homogéneos según su uso del campus virtual y establece relaciones con el rendimiento académico.

          Translated abstract

          Introduction. Virtual learning environments enable users to create dynamics and learning facilitator spaces. To investigate the students’ use can identify patterns and help to an early detection of students at high risk of dropping out since correlations between its use and the academic performance have been described. Materials and methods. Seven virtualized spaces corresponding to four courses from three Health Sciences degrees taught in 2017/18 and 2018/19, with a total of 517 students were studied. Previously, logs had been extracted from every space, debugged and anonymized. Number of logins, of access to resources and to URL as well as the forums use were considered. A multiple correspondence analysis was applied followed by a hierarchical clustering analysis. Results. 4 clusters, with sizes between 20.9% and 29.4% of the students, were obtained and characterized by differential behaviors of the virtual campus use. Relationships can be established with final grades as well as theory’ and practical’ grades. Results pointed out that the lower interaction in virtual campus, the lower the grades, while the higher interaction, the higher the marks. Conclusions. Our study pinpoints different student clusters with homogeneous virtual campus behavior and establishes relationships with the academic performance.

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          Most cited references34

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              Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

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                Author and article information

                Journal
                fem
                FEM: Revista de la Fundación Educación Médica
                FEM (Ed. impresa)
                Fundación Educación Médica y Viguera Editores, S.L. (Barcelona, Barcelona, Spain )
                2014-9832
                2014-9840
                2021
                : 24
                : 6
                : 317-321
                Affiliations
                [2] Madrid Madrid orgnameUniversidad Complutense de Madrid orgdiv1Facultad de Enfermería, Fisioterapia y Podología orgdiv2Departamento de Enfermería Spain
                [3] Madrid Madrid orgnameUniversidad Complutense de Madrid orgdiv1Área de Gobierno de Tecnologías de la Información y Apoyo Técnico al Usuario Spain
                [1] Madrid Madrid orgnameUniversidad Complutense de Madrid orgdiv1Facultad de Medicina orgdiv2Sección Departamental de Biología Celular Spain
                Article
                S2014-98322021000600317 S2014-9832(21)02400600317
                10.33588/fem.246.1159
                0c7d41c4-7d00-40ca-97d3-e694229124a7

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 15 July 2021
                : 17 November 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 35, Pages: 5
                Product

                SciELO Spain

                Categories
                Originales

                Health Sciences,Moodle,Learning analytics,Higher education,Cluster analysis,Academic performance,Rendimiento académico,Educación superior,Ciencias de la Salud,Analítica del aprendizaje,Análisis de conglomerados

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