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Volume 11
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10.3390/app11062677
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Article

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

by
María Consuelo Sáiz-Manzanares
1,*,
Juan José Rodríguez-Díez
2,
José Francisco Díez-Pastor
2,
Sandra Rodríguez-Arribas
2,
Raúl Marticorena-Sánchez
2,* and
Yi Peng Ji
2
1
Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Research Group DATAHES, P° Comendadores s/n, 09001 Burgos, Spain
2
Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group ADMIRABLE, Escuela Politécnica Superior, Avda. de Cantabria s/n, 09006 Burgos, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2021, 11(6), 2677; https://doi.org/10.3390/app11062677
Submission received: 16 February 2021 / Revised: 7 March 2021 / Accepted: 13 March 2021 / Published: 17 March 2021
(This article belongs to the Special Issue Application of Technologies in E-learning Assessment)
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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.
Keywords: at-risk student; clustering; visualisation; self-regulated learning; Moodle; learning analytics at-risk student; clustering; visualisation; self-regulated learning; Moodle; learning analytics

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MDPI and ACS Style

Sáiz-Manzanares, M.C.; Rodríguez-Díez, J.J.; Díez-Pastor, J.F.; Rodríguez-Arribas, S.; Marticorena-Sánchez, R.; Ji, Y.P. Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques. Appl. Sci. 2021, 11, 2677. https://doi.org/10.3390/app11062677

AMA Style

Sáiz-Manzanares MC, Rodríguez-Díez JJ, Díez-Pastor JF, Rodríguez-Arribas S, Marticorena-Sánchez R, Ji YP. Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques. Applied Sciences. 2021; 11(6):2677. https://doi.org/10.3390/app11062677

Chicago/Turabian Style

Sáiz-Manzanares, María Consuelo, Juan José Rodríguez-Díez, José Francisco Díez-Pastor, Sandra Rodríguez-Arribas, Raúl Marticorena-Sánchez, and Yi Peng Ji. 2021. "Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques" Applied Sciences 11, no. 6: 2677. https://doi.org/10.3390/app11062677

APA Style

Sáiz-Manzanares, M. C., Rodríguez-Díez, J. J., Díez-Pastor, J. F., Rodríguez-Arribas, S., Marticorena-Sánchez, R., & Ji, Y. P. (2021). Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques. Applied Sciences, 11(6), 2677. https://doi.org/10.3390/app11062677

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