Automatic Deduction of Learners’ Profiling Rules Based on Behavioral Analysis

Abstract

E-learning has become a more flexible learning approach thanks to the extensive evolution of the Information and Communication Technologies. A perceived focus was investigated for the exploitation of the learners’ individual differences to ensure a continuous and adapted learning process. Nowadays, researchers have been oriented to use learning analytics for learner modeling in order to assist educational institutions in improving learner success and increasing learner retention. In this paper, we describe a new implicit approach using learning analytics to construct an interpretative views of the learners’ interactions, even those made outside the E-learning platform. We aim to deduce automatically a learners’ profiling rules independently of the learning style models proposed in the literature. In this way, we provide an innovative process that may help the tutors to profile learners and evaluate their performances, support the courses’ designer in their authoring tasks and adapt the learning objects to the learners’ needs.