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

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.

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques

“…it has been found that the behavioural profiles within each collaborative group, that are represented in Heat Map, do not have a homogeneous pattern of interaction between the members of each collaborative group and that there are always one or two members in each group who set the pace of work (Dobashi et al, 2019;Sáiz-Manzanares et al, 2020b). Therefore, it can be concluded that monitoring the learning process in each student is essential throughout the entire development for the detection of students at risk, especially in the initial and intermediate phases of the learning process (Bannert et al, 2014;Bogarín et al, 2018;Cerezo et al, 2016;Sáiz-Manzanares et al, 2021b). Ideally, an initial measurement should be taken two weeks into the course, an intermediate measurement (in the middle of the course) and a final measurement (one or two weeks before the end of the course).…”

“…Recent studies (Park & Jo, 2017 ) have found significant differences in learning outcomes according to the teachers’ teaching style and the learning style of students (Sáiz-Manzanares et al, 2021a ). Another relevant aspect facilitated by LMSs is the early detection of at-risk students, Strang ( 2016 ) analyses the relationship between the use of LMSs and students' learning patterns on the platform (Sáiz-Manzanares et al, 2021b ). In this line, regression analysis techniques, among others, make it possible to detect successful and risky behavioural patterns.…”

COVID-19 Emergency Remote Teaching Opinions and Academic Performance of Undergraduate Students: Analysis of 4 Students’ Profiles. A Case Study

“…Monitoring their learning and enables the application of personalized actions for each individual student according to their profile. A software has been recently patented to monitor student learning in the Moodle platform [27].…”

Valoración de la Ecología de Aprendizaje Autorregulado Virtualizada para la Didáctica de las Ciencias de la Naturaleza durante la crisis COVID-19

“…Para lograrlo deben incluir, entre otros: a) un análisis de los conceptos previos del alumnado con el fin de diseñar distintos niveles de dificultad; b) tareas de aprendizaje que incluyan un diseño de aprendizaje por descubrimiento; y c) retroalimentación orientada a procesos para que el aprendiz pueda aprender del error. Todas estas funcionalidades potenciarán un aprendizaje personalizado acorde con el ritmo de aprendizaje de cada estudiante (Sáiz-Manzanares et al, 2021, Sáiz-Manzanares et al, 2019bVázquez-Dorrío, 2016;Vázquez-Dorrío & Vázquez-Dorrío, 2018).…”

“…En estos contextos combinados en los que se promovió el aprendizaje autorregulado del alumnado, este presentó un aumento significativo en los procesos de autorregulación del aprendizaje y percibieron un mayor apoyo por parte del profesorado (Martínez-Sarmiento & González, 2019). Del mismo modo, en experiencias de aprendizaje autorregulado realizadas durante el confinamiento, el seguimiento del alumnado y la intervención personalizada dio lugar a bajas tasas de abandono y alta satisfacción del alumnado con el proceso de enseñanza-aprendizaje (Sáiz-Manzanares et al, 2021).…”