Abstract—When leaning, be it a face to face or online, students favor a customized learning that meets their needs and preferences. Learners are more motivated and their evaluation results are satisfactory. Indeed, the adaptation of interventions according to the learning profiles of student is one of the best ways to improve learning. However, a learning profile is easier to detect in a face to face learning situation rather than in an online learning situation, especially when the defining rules the different profiles are imprecise and difficult to formulate in a digital language. In our contribution, we aim to solve this problem by proposing a profile deduction system allowing to translate the performance rules provided by the expert into numerical rules manipulated by the machine, which will facilitate the deduction of learning profiles from interactions made by learners face to training. For this, we will use the algorithm classification ANTClust. An experiment part is proposed to verify the accuracy of the classification performed and the obtained results.
Index Terms—E-Learning, deduction system of learning profiles, performance indicators, classification, ANTClust algorithm.
The authors are with the Team: Modeling and Optimization for Mobile Services, Ain Chock Hassan II University– Faculty of Sciences (e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
Cite: Fatima-Zahra Ammor, Driss Bouzidi, and Amina Elomri, "Construction of Deduction System of Learning Profile from Performance Indicators," International Journal of Information and Education Technology vol. 3, no. 2, pp. 129-134, 2013.