Importance of Dynamic Identification of Learning Style on the Efficacy of Adaptive e-Learning Systems

Abstract

This article presents an ongoing research project on the development of adaptive e-Learning systems in relation to the theory of learning style. Previously, a literature review has been conducted on learning style modeling methods, the Felder-Silverman learning style model (FSLSM), and the main problems encountered with the current traditional adaptive e-learning system (TAES). In this work a proposed intelligent adaptive e-learning system (PIAES) is designed to dynamically detect students’ learning styles, and to inform appropriate learning material design. An experiment was conducted in order to validate the system’s ability to detect students' learning styles and to enhance their academic achievements relative to those of a traditional adaptive e-learning system (TAES). Twenty-three students from the University of Jordan completed the Index of Learning Style (ILS) questionnaire and used the PIAES. The precision in learning style detection was satisfactory. The study also used a quasi-experimental design to validate the effectiveness of the PIAES in enhancing academic achievements relative to those of the TAES. A group of 110 students were recruited from the University of Jordan and randomly assigned to a control (56 students) or experimental group (54 students). Students in the experimental group (PIAES) showed better academic achievement relative to that of students in the control group (TAES). These results show that the proposed adaption strategies presented in the PIAES could improve student achievement because it adapts to each student’s learning style and dynamically tailor learning material based on learner responses.