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Elman Neural Network Trained by using Artificial Bee Colony for the Classification of Learning Style based on Students Preferences |
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PP: 1269-1278 |
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doi:10.18576/amis/110504
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Author(s) |
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Nor Liyana Mohd Shuib,
Ahmad Shukri Mohd Noor,
Haruna Chiroma,
Tutut Herawan,
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Abstract |
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| 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. |
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