Improving online education through automatic learning style identification using a multi-step architecture with ant colony system and artificial neural networks

Abstract

Learning style is one of the individual differences which play an important role in learning. Being aware of it helps the student to understand their strengths and weaknesses, and the teacher to provide more valuable personalized interventions. Furthermore, learning style-based adaptive educational systems can be designed, which have been shown to increase student satisfaction or learning gain, while reducing the time needed to learn. It is therefore important to have an accurate method for identifying students’ learning styles. Since the traditional approach of filling in dedicated psychological questionnaires has several disadvantages, automatic methods have been proposed, based on investigating student observable behavior in a learning environment. Research done so far generally takes a mono-algorithmic approach to identify learning styles, and the precision rates leave room for improvement. Hence, in this paper we propose a novel hybrid multi-step architecture based on ant colony system and artificial neural networks to increase the precision of learning styles identification. Two different variants are proposed and evaluated with data from 75 students; results show high precision values, outperforming existing automatic approaches for learning style identification. The proposed architecture can be integrated into widely used educational systems (e.g., learning management systems) to provide learners and/or teachers with information about students’ learning styles. In addition, it can be integrated into adaptive educational systems and plugins of learning management systems to automatically identify learning styles and personalize instruction respectively.