Multi layer feed forward artificial neural network for learning styles identification

Accommodating learning styles in adaptive educational hypermedia system (AEHS) may lead to an increased effectiveness and efficiency of the learning process as well as teacher and learner satisfaction. The premise is that a fact that learning in the classroom is less efficient, when teachers will no...

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Main Author:	Bayasut, Bilal Luqman
Format:	Thesis
Language:	English English
Published:	2015
Subjects:	Q Science (General) QA Mathematics QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/16821/1/Multi%20Layer%20Feed%20Forward%20Artificial%20Neural%20Network%20For%20Learning%20Styles%20Identification.pdf http://eprints.utem.edu.my/id/eprint/16821/2/Multi%20layer%20feed%20forward%20artificial%20neural%20network%20for%20learning%20styles%20identification.pdf
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id	my-utem-ep.16821
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Ananta, Gede Pramudya
topic	Q Science (General) QA Mathematics QA76 Computer software
spellingShingle	Q Science (General) QA Mathematics QA76 Computer software Bayasut, Bilal Luqman Multi layer feed forward artificial neural network for learning styles identification
description	Accommodating learning styles in adaptive educational hypermedia system (AEHS) may lead to an increased effectiveness and efficiency of the learning process as well as teacher and learner satisfaction. The premise is that a fact that learning in the classroom is less efficient, when teachers will not be able to get insight of each of the student’s learning style; hence, they won't be able to adapt their teaching strategies to match with the student’s learning style. In order to get an insight of the student’s learning style in AEHS, the system must be able to recognize the learning styles of the students. Current methods for recognizing learning styles are less efficient, where questionnaires will lead to tedium and disturbance at learning processes. Thus, this study developed the learning styles based AEHS that utilized Multi Layer Feed-Forward Artificial Neural Network (MLFF) which was used to identify student’s learning styles in real-time. The automatic and real-time learning styles identification was done by analyzing the student’s browsing behavior while they are learning through the proposed AEHS. The system then adaptively presents the learning content that matches with the students’ learning styles by the means of fragment sorting and adaptive annotation technique. At the end of the study, the data triangulation was done to test if incorporating learning styles in learning environments can impact the student achievement. It was done by asking the student to answer the mini quiz after they were using the proposed AEHS with adaptive feature was activated. This study also focused on analysis of the existence of the relationship between the frequencies of students’ click on learning components with their staying time on those particular learning components. The result showed that the proposed MLFF performed well in identifying the students’ learning styles in real-time. Moreover, the analyzed student’s browsing behavior revealed that there was a relationship between the frequencies of the students’ click on learning components with their staying time on those particular components. Furthermore, after the student’s learnt through the proposed AEHS with adaptive feature activated and answered the mini quiz result; most of them could achieve the perfect score. In this case, the mini quiz result showed that incorporating learning styles into learning environment may affect and increase student’s achievements.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Bayasut, Bilal Luqman
author_facet	Bayasut, Bilal Luqman
author_sort	Bayasut, Bilal Luqman
title	Multi layer feed forward artificial neural network for learning styles identification
title_short	Multi layer feed forward artificial neural network for learning styles identification
title_full	Multi layer feed forward artificial neural network for learning styles identification
title_fullStr	Multi layer feed forward artificial neural network for learning styles identification
title_full_unstemmed	Multi layer feed forward artificial neural network for learning styles identification
title_sort	multi layer feed forward artificial neural network for learning styles identification
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Information And Communication Technology
publishDate	2015
url	http://eprints.utem.edu.my/id/eprint/16821/1/Multi%20Layer%20Feed%20Forward%20Artificial%20Neural%20Network%20For%20Learning%20Styles%20Identification.pdf http://eprints.utem.edu.my/id/eprint/16821/2/Multi%20layer%20feed%20forward%20artificial%20neural%20network%20for%20learning%20styles%20identification.pdf
_version_	1747833896581464064
spelling	my-utem-ep.168212022-06-07T13:07:40Z Multi layer feed forward artificial neural network for learning styles identification 2015 Bayasut, Bilal Luqman Q Science (General) QA Mathematics QA76 Computer software Accommodating learning styles in adaptive educational hypermedia system (AEHS) may lead to an increased effectiveness and efficiency of the learning process as well as teacher and learner satisfaction. The premise is that a fact that learning in the classroom is less efficient, when teachers will not be able to get insight of each of the student’s learning style; hence, they won't be able to adapt their teaching strategies to match with the student’s learning style. In order to get an insight of the student’s learning style in AEHS, the system must be able to recognize the learning styles of the students. Current methods for recognizing learning styles are less efficient, where questionnaires will lead to tedium and disturbance at learning processes. Thus, this study developed the learning styles based AEHS that utilized Multi Layer Feed-Forward Artificial Neural Network (MLFF) which was used to identify student’s learning styles in real-time. The automatic and real-time learning styles identification was done by analyzing the student’s browsing behavior while they are learning through the proposed AEHS. The system then adaptively presents the learning content that matches with the students’ learning styles by the means of fragment sorting and adaptive annotation technique. At the end of the study, the data triangulation was done to test if incorporating learning styles in learning environments can impact the student achievement. It was done by asking the student to answer the mini quiz after they were using the proposed AEHS with adaptive feature was activated. This study also focused on analysis of the existence of the relationship between the frequencies of students’ click on learning components with their staying time on those particular learning components. The result showed that the proposed MLFF performed well in identifying the students’ learning styles in real-time. Moreover, the analyzed student’s browsing behavior revealed that there was a relationship between the frequencies of the students’ click on learning components with their staying time on those particular components. Furthermore, after the student’s learnt through the proposed AEHS with adaptive feature activated and answered the mini quiz result; most of them could achieve the perfect score. In this case, the mini quiz result showed that incorporating learning styles into learning environment may affect and increase student’s achievements. 2015 Thesis http://eprints.utem.edu.my/id/eprint/16821/ http://eprints.utem.edu.my/id/eprint/16821/1/Multi%20Layer%20Feed%20Forward%20Artificial%20Neural%20Network%20For%20Learning%20Styles%20Identification.pdf text en public http://eprints.utem.edu.my/id/eprint/16821/2/Multi%20layer%20feed%20forward%20artificial%20neural%20network%20for%20learning%20styles%20identification.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=96167 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Information And Communication Technology Ananta, Gede Pramudya 1. Akbulut, Y. and Cardak, C.S. 2012. Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers & Education 58(2), pp. 835–842. 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