Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman

Student learning style is the tendency of a student's approach to learn. Most of the conventional methods to determine students’ learning style are by inquiry and survey methods that require students to answer several questions. This conventional method has several disadvantages such as requiri...

Full description

Bibliographic Details
Main Authors: Mohd Faisal Ibrahim, Fatimah Az Zahra Azizan, Mohd Saiful Dzulkefly Zan
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/21341/
http://journalarticle.ukm.my/21341/1/AJ%208.pdf
_version_ 1848815330500018176
author Mohd Faisal Ibrahim,
Fatimah Az Zahra Azizan,
Mohd Saiful Dzulkefly Zan,
author_facet Mohd Faisal Ibrahim,
Fatimah Az Zahra Azizan,
Mohd Saiful Dzulkefly Zan,
author_sort Mohd Faisal Ibrahim,
building UKM Institutional Repository
collection Online Access
description Student learning style is the tendency of a student's approach to learn. Most of the conventional methods to determine students’ learning style are by inquiry and survey methods that require students to answer several questions. This conventional method has several disadvantages such as requiring an amount of time to answer questions, questions are answered casually, low quality of survey questions and a different way each student interprets the questions given. With the development of today's e-learning technology such as the digital learning management system (LMS), records of student activities and interactions with the learning system can be stored online. This study aims to examine the relationship between students’ activities in a LMS in determining their learning style. Based on the Felder-Silverman learning style theory, an artificial intelligence model has been developed to automatically determine the learning style dimensions by using student activity records in the LMS. Artificial intelligence models are built based on neural network algorithms and the supervised machine learning technique. These neural network models have been trained using records of how often a student visits learning content such as lecture notes, learning videos, teaching slides and online exercises. As a result, four neural network models have been produced, each of which represents the dimensions of the Felder-Silverman learning style, namely information reception, information delivery, information processing and information organisation. The accuracy of the model obtained is between 77.8% to 100%.
first_indexed 2025-11-15T00:48:16Z
format Article
id oai:generic.eprints.org:21341
institution Universiti Kebangasaan Malaysia
institution_category Local University
language English
last_indexed 2025-11-15T00:48:16Z
publishDate 2022
publisher Penerbit Universiti Kebangsaan Malaysia
recordtype eprints
repository_type Digital Repository
spelling oai:generic.eprints.org:213412023-03-13T05:41:40Z http://journalarticle.ukm.my/21341/ Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman Mohd Faisal Ibrahim, Fatimah Az Zahra Azizan, Mohd Saiful Dzulkefly Zan, Student learning style is the tendency of a student's approach to learn. Most of the conventional methods to determine students’ learning style are by inquiry and survey methods that require students to answer several questions. This conventional method has several disadvantages such as requiring an amount of time to answer questions, questions are answered casually, low quality of survey questions and a different way each student interprets the questions given. With the development of today's e-learning technology such as the digital learning management system (LMS), records of student activities and interactions with the learning system can be stored online. This study aims to examine the relationship between students’ activities in a LMS in determining their learning style. Based on the Felder-Silverman learning style theory, an artificial intelligence model has been developed to automatically determine the learning style dimensions by using student activity records in the LMS. Artificial intelligence models are built based on neural network algorithms and the supervised machine learning technique. These neural network models have been trained using records of how often a student visits learning content such as lecture notes, learning videos, teaching slides and online exercises. As a result, four neural network models have been produced, each of which represents the dimensions of the Felder-Silverman learning style, namely information reception, information delivery, information processing and information organisation. The accuracy of the model obtained is between 77.8% to 100%. Penerbit Universiti Kebangsaan Malaysia 2022-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/21341/1/AJ%208.pdf Mohd Faisal Ibrahim, and Fatimah Az Zahra Azizan, and Mohd Saiful Dzulkefly Zan, (2022) Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman. AJTLHE: ASEAN Journal of Teaching and Learning in Higher Education, 14 (2). pp. 108-121. ISSN 1985-5826 https://ejournal.ukm.my/ajtlhe/
spellingShingle Mohd Faisal Ibrahim,
Fatimah Az Zahra Azizan,
Mohd Saiful Dzulkefly Zan,
Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman
title Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman
title_full Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman
title_fullStr Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman
title_full_unstemmed Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman
title_short Model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model Felder-Silverman
title_sort model rangkaian neural bagi penentuan gaya pembelajaran pelajar berasaskan model felder-silverman
url http://journalarticle.ukm.my/21341/
http://journalarticle.ukm.my/21341/
http://journalarticle.ukm.my/21341/1/AJ%208.pdf