Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques

  1. Ji, Yi Peng
  2. Rodríguez-Díez, Juan José
  3. Díez-Pastor, José Francisco
  4. Marticorena-Sánchez, Raúl
  5. Sáiz-Manzanares, María Consuelo
  6. Rodríguez-Arribas, Sandra
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos
Revista:
Applied Sciences

ISSN: 2076-3417

Año de publicación: 2021

Volumen: 11

Número: 6

Páginas: 2677

Tipo: Artículo

DOI: 10.3390/APP11062677 GOOGLE SCHOLAR

Otras publicaciones en: Applied Sciences

Resumen

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.

Referencias bibliográficas

  • 10.3390/ijerph17155618
  • 10.3390/app10103469
  • 10.5209/rev_RCED.2017.v28.n2.50711
  • 10.1016/j.future.2018.08.003
  • 10.3102/1076998616666808
  • 10.1002/cae.21782
  • 10.1007/978-3-319-98140-6
  • 10.1109/ICCCNT45670.2019.8944511
  • 10.1109/TSMCC.2010.2053532
  • 10.1016/j.compedu.2017.05.021
  • 10.1108/IDD-08-2018-0039
  • 10.1109/iSAI-NLP48611.2019.9045576
  • 10.3390/buildings9050103
  • 10.1016/j.aei.2019.100927
  • 10.1155/2015/340521
  • 10.7334/psicothema2018.330
  • 10.3389/fpsyg.2017.00745
  • 10.1007/s11618-019-00912-1
  • 10.1007/978-3-662-55993-2
  • 10.1016/j.compedu.2016.02.006
  • 10.1109/TELSIKS46999.2019.9002177
  • 10.18421/TEM91-47
  • 10.1109/ICODSE.2017.8285882
  • 10.1109/ICALT.2019.00040
  • 10.1109/EDUCON.2019.8725062
  • 10.23919/MIPRO.2018.8400123
  • 10.1007/978-981-13-6908-7_19
  • 10.24507/icicelb.10.11.963
  • 10.1007/978-3-030-11932-4_33
  • 10.1007/978-3-030-23884-1_16
  • 10.35940/ijrte.B2030.078219
  • 10.1016/j.compedu.2007.05.016
  • 10.1109/ACCESS.2020.3014948
  • 10.1109/INCIT.2019.8912068
  • UBU Monitor: Monitoring of Students on the Moodle Platformhttps://github.com/yjx0003/UBUMonitor
  • 10.5220/0006318403390346
  • 10.1080/0142159X.2017.1309376
  • 10.1007/978-3-319-10247-4
  • Fernández, (1993), Cad. Aten. Primaria, 3, pp. 138
  • 10.1007/3-540-58108-1_24
  • No TitlMath-Commons Math: The Apache Commons Mathematics Libraryehttps://commons.apache.org/proper/commons-math/
  • Smile Statistical Machine Intelligence and Learning Enginehttps://haifengl.github.io/
  • Sáiz-Manzanares, (2018)
  • Bol-Arreba, (2013), Open Classr., 41, pp. 45
  • 10.1007/1-4020-5742-3
  • Student Opinion Survey on the Quality of Teaching (QSOQT)https://cutt.ly/pjiD6iN
  • (2016)
  • (2016)
  • (2020)
  • Bandalos, (2001), pp. 269