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 use of the tool was tested with groups of students of the algorithm course in the periods 2017-1, 2017-2, 2018-1 and 2018-2, with a total of 90 students, in parallel with the control groups in the same periods, conformed by 95 students, obtaining superior averages in the test groups vs the control groups, which evidenced the functionality and utility of the software.