An Analytics Dashboard for Personalised E-learning: A Preliminary Study

Abstract

Over the last few decades, information and communication technologies (ICT) have changed our lives to enhance the process of teaching and learning. E-learning is powerful and influential in the classroom or elsewhere, as long as there is computer and internet access. The vast majority of institutions utilized a Learning Management System (LMS) to administer online courses to create, deliver, moderate and facilitate academic content and activities. While LMSs are mainly being used as a repository for course materials and platforms for assessing learning, recent developments require e-learning to be more responsive to students’ needs for a more customized learning experience. This requires functional characteristics like personalization analytics, self-monitoring, and intervention. Besides being a primary learning source nowadays, e-learning has been useful to monitor students’ performance and retention levels. However, the current e-learning system does not allow the user to empower the learning experience and the research on the suitability of the learning content is still lacking. We proposed a general design and implementation of a learning analytics dashboard for students comprising a predictive analytic component that is useful to help monitor or predict their academic performance based on learning activities. The results show that the system, known as DashLearn, allows students to stay alert of their current academic performance compared to their peers, monitor attendance and assignment submission status, and predict their grade ahead of time for an early intervention leading to more optimized learning.