Efforts have been made in recent times by educators and researchers to provide learners with appropriate learning objects (LO) based on their learning style (LS). Previous studies on the classification of LS, typically classifyLS based on the description of the LS preference itself without giving attention to the student preferences.This study presents a new knowledge in classifying learning material based on learning style. In this paper, we propose Elman Neural Network (ENN) trained by using Artificial Bee Colony (ABC) (ABCENN) to create a classifier for the classification of LS (Diverging, Accommodating, Converging, and Assimilating) based on student preference of teaching strategies (TS) and LO. Our research extends on ourprevious work which considered only LO without TS. For the purpose of comparison, hybrid of ABC and backpropagation neural network (ABCBPNN) and ENN were applied to classify the LS of learners. Simulation results indicated that the propose ABCENN classifier outperforms ABCBPNN, and ENN classifiers with an accuracy of 97.12% and converges faster than the comparison methods. The propose ABCENN of this research can offer valuable information for educators, school administrators, and researchers to reach a decision on their respective students and to appropriately adapt their teaching methods.This in turn can significantly improve learners performance in understanding the subject matter.
Digital Object Identifier (DOI)
Liyana Mohd Shuib, Nor; Shukri Mohd Noor, Ahmad; Chiroma, Haruna; and Herawan, Tutut
"Elman Neural Network Trained by using Artificial Bee Colony for the Classification of Learning Style based on Students Preferences,"
Applied Mathematics & Information Sciences: Vol. 11:
5, Article 4.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol11/iss5/4