2024-03-29T06:42:41Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/62412022-11-30T13:21:11Zcom_10259_4219com_10259_5086com_10259_2604com_10259_5841col_10259_4220col_10259_5842
Sáiz Manzanares, María Consuelo
Rodríguez Diez, Juan José
Diez Pastor, José Francisco
Rodríguez Arribas, Sandra
Marticorena Sánchez, Raúl
Ji, Yi Peng
2021-03
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.
application/pdf
http://hdl.handle.net/10259/6241
eng
MDPI
Monitoring of Student Learning in Learning Management Systems: An Application of Educational Data Mining Techniques
info:eu-repo/semantics/article
TEXT
RIUBU. Repositorio Institucional de la Universidad de Burgos
Hispana