Implementation of an Adaptive Mechanism in Moodle Based on a Hybrid Dynamic User Model

Abstract

Learning styles summarizes the concept that individuals have different learning preferences and they learn better when they receive information in their preferred way. Even though learning styles have been subjected to some criticism, they can play an important role in adaptive e-learning systems. In order to overcome the drawbacks of the traditional detection method, educational data mining techniques have been implemented in these systems for the automatic detection of students’ learning styles. The purpose of this paper is to present the implementation of an adaptive mechanism in Moodle. The proposed mechanism is based on a hybrid dynamic user model that is built with techniques that are based both on learner knowledge and behavior. An evaluation study was conducted in order to examine the effectiveness of the proposed mechanism. The results were encouraging since they indicated that our extension affected students’ motivation and performance. In addition, the precision attained by the proposed automatic detection approach was rather positive.