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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6241

    Título
    Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
    Autor
    Sáiz Manzanares, María ConsueloUBU authority Orcid
    Rodríguez Diez, Juan JoséUBU authority Orcid
    Diez Pastor, José FranciscoUBU authority Orcid
    Rodríguez Arribas, SandraUBU authority Orcid
    Marticorena Sánchez, RaúlUBU authority Orcid
    Ji, Yi Peng
    Publicado en
    Applied Sciences. 2021, V. 11, n. 6, 2677
    Editorial
    MDPI
    Fecha de publicación
    2021-03
    ISSN
    2076-3417
    DOI
    10.3390/app11062677
    Abstract
    In this study, we used a module for monitoring and detecting students at risk of dropping out. We worked with a sample of 49 third-year students in a Health Science degree during a lockdown caused by COVID-19. Three follow-ups were carried out over a semester: an initial one, an intermediate one and a final one with the UBUMonitor tool. This tool is a desktop application executed on the client, implemented with Java, and with a graphic interface developed in JavaFX. The application connects to the selected Moodle server, through the web services and the REST API provided by the server. UBUMonitor includes, among others, modules for log visualisation, risk of dropping out, and clustering. The visualisation techniques of boxplots and heat maps and the cluster analysis module (k-means ++, fuzzy k-means and Density-based spatial clustering of applications with noise (DBSCAN) were used to monitor the students. A teaching methodology based on project-based learning (PBL), self-regulated learning (SRL) and continuous assessment was also used. The results indicate that the use of this methodology together with early detection and personalised intervention in the initial follow-up of students achieved a drop-out rate of less than 7% and an overall level of student satisfaction with the teaching and learning process of 4.56 out of 5.
    Palabras clave
    At-risk student
    Clustering
    Visualisation
    Self-regulated learning
    Moodle
    Learning analytics
    Materia
    Enseñanza superior
    Education, Higher
    Informática
    Computer science
    Psicología
    Psychology
    URI
    http://hdl.handle.net/10259/6241
    Versión del editor
    https://doi.org/10.3390/app11062677
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