Personalized Tutoring Model Through the Application of Learning Analytics Phases

Abstract

Learning Analytics (LA) have a significant impact in learning and teaching processes. These can be improved using the available data retrieved from the students’ activity inside the virtual classrooms of a LMS. This process requires the development of a tool that allows to handle the retrieved information properly. This paper presents a solution to this need, in the form of a development model and actual implementation of a LA tool. Four phases are implemented (Explanation, Diagnosis, Prediction, Prescription) this app allows the teacher for tracking the students’ activity in a virtual classroom implemented in the Sakai LMS. It also allows for the identification of users with challenges in their academic process and the learning itinerary in combination with a personalized mentoring by the teacher or tutor. The developed software was implemented with a test group, consisted of 39 students, who achieved and average acore of 4.2 over 5.0; in parallel, the control group, got an average score of 3.8 over 5.0, which leads to some reflection about the system functionality and outcome.