Smart Courses Recommender System for Online Learning Platform

Abstract

The present work is a part of the ESTenLigne1project, which is the result of several years of experience for developing e-Iearning in the High School of Technology of Fez. It was started since 2012 by the EST network of Morocco. It aims the development of distance education based on new information and communication technologies through the implementation of open, adaptive and free e-Iearning platform. However, this platform faces many challenges, such as the increasing amount of data including the diversity of courses and a large number of learners that makes harder to find what the learners are really looking for. Furthermore, most of the students in this platform are new graduates who have just come to integrate higher education and who need a system to help them to find the relevant courses that take into account the requirements and needs of each learner. In this article, we develop a courses recommender system for the e-Iearning platform. It aims to discover relationships between student's courses activities using association rules mining method in order to help the student to choose the more appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner's behaviors and preferences. Next, we implement our system using the FP-growth algorithm and R programming language. Finally, the experimental results prove the effectiveness and reliability of the proposed system to increase the quality of student's decision, guide them during the learning process and provide targeted online learning courses to meet the needs of the learners.