The application of multi layer feed forward artificial neural network for learning style identification

Luqman Bayasut, Bilal and Pramudya, Gede and Basiron, Halizah (2014) The application of multi layer feed forward artificial neural network for learning style identification. Advanced Science Letters, 20 (10-12). pp. 2180-2183. ISSN 1936-6612

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Abstract

Accommodating learning styles in adaptive educational hypermedia systems (AEHS) improves students' learning performance in web-based learning. Hence, in implementing the systems, the students' learning style identification process is important. Most of today's AEHS rely on a traditional technique of identifying students' learning styles, which is using questionnaires. However, using questionnaires for this purpose is less efficient, cumbersome and may not be that feasible. This study proposes a real-time learning style identification technique by recording students' browsing behaviors and analyzing them by using multi-layer feed forward artificial neural network (MLFF). The result suggests that there is a relationship between the frequencies of students' click on learning components with their staying time on those components. It also indicates that the proposed identification technique performs well in identifying students' learning styles.

Item Type:	Article
Uncontrolled Keywords:	adaptive educational hypermedia systems, learning styles, browsing behavior, artificial neural network
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User:	DR PRAMUDYA ANANTA I GEDE
Date Deposited:	28 Jan 2015 00:45
Last Modified:	12 Jul 2023 12:28
URI:	http://eprints.utem.edu.my/id/eprint/14162
Statistic Details:	View Download Statistic

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