JavaScript is disabled for your browser. Some features of this site may not work without it.

    Listar

    Todo RIUMAComunidades & ColeccionesPor fecha de publicaciónAutoresTítulosMateriasTipo de publicaciónCentrosEsta colecciónPor fecha de publicaciónAutoresTítulosMateriasTipo de publicaciónCentros

    Mi cuenta

    AccederRegistro

    Estadísticas

    Ver Estadísticas de uso

    DE INTERÉS

    Datos de investigaciónReglamento de ciencia abierta de la UMAPolítica de RIUMAPolitica de datos de investigación en RIUMASHERPA/RoMEODulcinea
    Preguntas frecuentesManual de usoDerechos de autorContacto/Sugerencias
    Ver ítem 
    •   RIUMA Principal
    • Investigación
    • Lenguajes y Ciencias de la Computación - (LCC)
    • LCC - Contribuciones a congresos científicos
    • Ver ítem
    •   RIUMA Principal
    • Investigación
    • Lenguajes y Ciencias de la Computación - (LCC)
    • LCC - Contribuciones a congresos científicos
    • Ver ítem

    Machine and Human Observable Differences in Groups’ Collaborative Problem-Solving Behaviours

    • Autor
      Cukurova, Mutlu; Luckin, Rose; Mavrikis, Manolis; Millan-Valldeperas, EvaAutoridad Universidad de Málaga
    • Fecha
      2019-10-18
    • Palabras clave
      Aprendizaje
    • Resumen
      This paper contributes to our understanding of how to design learning analytics to capture and analyse collaborative problem-solving (CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics. Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position ( fiducial marker) and heads direction (face recognition) data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill defi ciencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have signifi cant implications on the design and implementation of future learning analytics systems.
    • URI
      https://hdl.handle.net/10630/18599
    • Compartir
      RefworksMendeley
    Mostrar el registro completo del ítem
    Ficheros
    2017 ECTEL (b).pdf (1.052Mb)
    Colecciones
    • LCC - Contribuciones a congresos científicos

    Estadísticas

    Ver Estadísticas de uso
    Buscar en Dimension
    REPOSITORIO INSTITUCIONAL UNIVERSIDAD DE MÁLAGA
    REPOSITORIO INSTITUCIONAL UNIVERSIDAD DE MÁLAGA
     

     

    REPOSITORIO INSTITUCIONAL UNIVERSIDAD DE MÁLAGA
    REPOSITORIO INSTITUCIONAL UNIVERSIDAD DE MÁLAGA