Universidad de Burgos RIUBU Principal Default Universidad de Burgos RIUBU Principal Default
  • español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
Universidad de Burgos RIUBU Principal Default
  • Ayuda
  • Contacto
  • Sugerencias
  • Acceso abierto
    • Archivar en RIUBU
    • Acuerdos editoriales para la publicación en acceso abierto
    • Controla tus derechos, facilita el acceso abierto
    • Sobre el acceso abierto y la UBU
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Listar

    Todo RIUBUComunidadesFechaAutor / DirectorTítuloMateria / AsignaturaEsta colecciónFechaAutor / DirectorTítuloMateria / Asignatura

    Mi cuenta

    AccederRegistro

    Estadísticas

    Ver Estadísticas de uso

    Compartir

    Ver ítem 
    •   RIUBU Principal
    • E-Prints y Datos de investigación
    • Grupos de investigación
    • Data Analysis Techniques Applied in health environments sciences (DATAHES)
    • Artículos DATAHES
    • Ver ítem
    •   RIUBU Principal
    • E-Prints y Datos de investigación
    • Grupos de investigación
    • Data Analysis Techniques Applied in health environments sciences (DATAHES)
    • Artículos DATAHES
    • Ver ítem

    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/10274

    Título
    Using Serious Game Techniques with Health Sciences and Biomedical Engineering Students: An Analysis Using Machine Learning Techniques
    Autor
    Sáiz Manzanares, María ConsueloAutoridad UBU Orcid
    Marticorena Sánchez, RaúlAutoridad UBU Orcid
    Escolar Llamazares, María del CaminoAutoridad UBU Orcid
    González Díez, IreneAutoridad UBU Orcid
    Velasco Saiz, Rut
    Publicado en
    Computers. 2024, V. 15, n. 12, 804
    Editorial
    MDPI
    Fecha de publicación
    2024
    DOI
    10.3390/info15120804
    Resumen
    The use of serious games on virtual learning platforms as a learning support resource is increasingly common. They are especially effective in helping students acquire mainly applied curricular content. However, a process is required to monitor the effectiveness and students’ perceived satisfaction. The objectives of this study were to (1) identify the most significant characteristics; (2) determine the most relevant predictors of learning outcomes; (3) identify groupings with respect to the different serious game activities; and (4) to determine students’ perceptions of the usefulness of the simple and complex serious game activities. We worked with a sample of 130 university students studying health sciences and biomedical engineering. The serious game activities were applied in a Moodle environment, UBUVirtual, and monitored using the UBUMonitor tool. The degree type and the type of serious game explained differing percentages of the variance in the learning results in the assessment tests (34.4%—multiple choice tests [individual assessment]; 11.2%—project performance [group assessment]; 25.6%—project presentation [group assessment]). Different clusters were found depending on the group of students and the algorithm applied. The Adjusted Rang Index was applied to determine the most appropriate algorithm in each case. The student satisfaction was high in all the cases. However, they indicated complex serious games as being more useful than simple serious games as learning resources for the practical content in both health sciences and biomedical engineering degrees.
    Palabras clave
    Serious games
    Machine learning
    Learning monitoring
    Branching scenario
    Materia
    Tecnología
    Technology
    Informática
    Computer science
    Psicología
    Psychology
    Enseñanza superior
    Education, Higher
    URI
    http://hdl.handle.net/10259/10274
    Versión del editor
    https://doi.org/10.3390/info15120804
    Aparece en las colecciones
    • Artículos DATAHES
    Atribución 4.0 Internacional
    Documento(s) sujeto(s) a una licencia Creative Commons Atribución 4.0 Internacional
    Ficheros en este ítem
    Nombre:
    Sáiz-information_2024.pdf
    Tamaño:
    6.110Mb
    Formato:
    Adobe PDF
    Thumbnail
    Visualizar/Abrir

    Métricas

    Citas

    Academic Search
    Ver estadísticas de uso

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis
    Mostrar el registro completo del ítem