###### 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 JavaEX. 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.  
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student's learning process must be included in LMSs in order to carry out early detection and to make proposals for personalised tutoring. This is an essential factor in achieving effective learning [3,4]. Nevertheless, the monitoring systems must be carried out with the use of technological resources and artificial intelligence techniques that facilitate the interpretation of the students' learning behaviours. Supervised learning such as predictive methods, and unsupervised learning such as clustering techniques have been shown to be very useful [5,6]. However, current LMSs, such as Moodle, do not have enough detection tools incorporated, because the learning analytics (LA) of the logs they offer is very simple and does not give the teacher sufficient information about these processes [7]. In particular, student drop-out at university is one of the major concerns of teachers and university vectors worldwide. Some studies [8] have analysed the possible causes and have specified that the factors can be related to students, teachers, the university system, or be an interaction of all of them. Among the causes specific to students, motivation stands out; amidst those specific to teachers, the quality of teaching and the enhancement of student motivation towards learning are the most common issues; and with respect to the institution, the quality of university management is the main aspect. The factors that may be influencing the lack of students' motivation which can be detected by technological systems included in the LMS are the frequency of access to the platform to different resources and activities. This frequency is obtained from the analysis of logs. The knowledge of these data throughout the learning process will be a very useful tool for a teacher in the prevention of school dropout [2]. Therefore, it is important to detect students at risk early, for which educational data mining (EDM) techniques can be applied [5,9]. These will allow the prediction of the profile of the student at risk [9,10]. It has been proven that with these techniques, the prediction percentage is in the range of 79-83% [11]. Additionally, it has been found that the use of teaching methodology in virtual environments based on self-regulated learning (SRL) [12] together with project-based learning (PBL) [13,14] and the use of continuous assessment methodology [15] are predictors of the achievement of effective learning (60.4%) [16] and decrease the dropout rate [15,17].