An e-Learning Recommendation System using Association Rule Mining Technique
The fast growth of e-learning around the world is inspiring many educational and business institutions to adopt the adaptive personalized method of teaching and learning. However the success of E-learning does not depend only on the academic setup but also on proper realization of its vital requisites. This paper presents an idea for building recommendation system for the e-learning system using Association Rule Mining Techniques for the best selection of e-learning resources or learning materials. This paper analyzes students' log of a Learning Management System (LMS) Moodle, and data gathered from a survey dataset addressing students academic, interaction and personal information. Data mining and statistical tools have been used to find relationships between students' LMS access behavior, study habits and overall performances.