Proposal for the Design and Evaluation of a Dashboard for the Analysis of Learner Behavior and Dropout Prediction in Moodle

Abstract

The rapid development of technology has meant that over the past two decades Information and Communications Technologies (ICT) become increasingly involved in the teaching process and seek to change traditional learning models. With the support of modern technology, virtual platforms that encourage the adoption of a new learning paradigm in which geographical/temporal limitations no longer pose a difficulty have been developed and refined. These virtual learning platforms, also known as Learning Management Systems (LMS), store student and teacher interactions with course resources, and these interactions are stored in database engines. However, all the information generated by LMS has not been processed in a way that is helpful for the use of teachers and students, mainly because in most cases, students' interactions with these systems focus on downloading class material, delivering assignments, and reading announcements, leaving aside indicators that can be presented in the form of visualizations that allow actions to be taken during the development of the learning process. Thus, this study proposes the design, implementation, and evaluation of a dashboard for the analysis of learner behavior and prediction of dropout on the Moodle platform. The proposed tool will help students to manage their learning process, easily and effectively monitor their progress in an online course, and teachers to know what students do before, during and after a virtual class. The latter for the purpose of being able to detect early students at risk of dropping out.