Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali

The electroencephalogram is an effective approach for measuring brainwaves and has been widely used to study mental performance such as learning and intelligence. Conventional assessment methods are exposed to reliability issues which stems from cultural and language barriers. Alternative approach b...

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Main Author:	Megat Ali, Megat Syahirul Amin
Format:	Book Section
Language:	English
Published:	Institute of Graduate Studies, UiTM 2018
Subjects:	Computers in education. Information technology Learning. Learning strategies
Online Access:	http://ir.uitm.edu.my/id/eprint/22224/ http://ir.uitm.edu.my/id/eprint/22224/1/ABS_MEGAT%20SYAHIRUL%20AMIN%20MEGAT%20ALI%20TDRA%20VOL%2014%20IGS%2018.pdf

id	uitm-22224
recordtype	eprints
spelling	uitm-222242018-11-19T04:22:06Z http://ir.uitm.edu.my/id/eprint/22224/ Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali Megat Ali, Megat Syahirul Amin Computers in education. Information technology Learning. Learning strategies The electroencephalogram is an effective approach for measuring brainwaves and has been widely used to study mental performance such as learning and intelligence. Conventional assessment methods are exposed to reliability issues which stems from cultural and language barriers. Alternative approach based on electroencephalogram has since been proposed; indicating correlation between resting brainwaves and learning styles. Validity of the findings however, was based on unconfirmed theories. Moreover, a systematic approach for learning style assessment based on brainwaves and advanced modelling technique as yet to be studied. Therefore, this research proposes an intelligent learning style classification model via brainwave features and artificial neural network. Eighty samples from various universities are segregated into four learning style groups based on Kolb's Learning Style Inventory. Twenty samples are identified as Divergers, twenty-two as Assimilators, twenty-one as Convergers and seventeen as Accommodators. Resting electroencephalogram is then recorded from the prefrontal region. Spectral centroid features from theta and alpha bands are then extracted for independent pattern analysis. Meanwhile, k-nearest neighbour is used for feature selection purposes. An intelligent learning style classification model is then constructed using spectral centroid features and multi-layered perceptron network. An independent dataset of fifty samples with varying levels of intelligence is used for a cross-relational mapping by the model. The pattern of features for each learning style group has shown correlation with the Neural Efficiency Hypothesis of intelligence. Subsequently, the fully developed model has attained excellent classification accuracy of 98.8% with mean squared error of 0.07. Moreover, the network has fulfilled all correlation requirements in classifying learning styles. The cross-relational analysis revealed that brighter individuals are predicted to be either Assimilative or Convergent. Meanwhile, the less brilliant ones are predicted to be either Divergent or Accommodative. Therefore, high level of intelligence is linked to excellent analytical skills, whereas low level of intelligence is associated with reliance on intuition rather than cognitive abilities. Conclusively, this thesis has proven that spectral centroid features from the resting brainwaves are suitable descriptors for characterising learning styles. The systematic approach established by the intelligent model provides an alternative for assessing the behaviour via electroencephalogram. Furthermore, the study has also confirmed that brainwaves from the prefrontal region are adequate for classification of learning styles. Institute of Graduate Studies, UiTM 2018 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/22224/1/ABS_MEGAT%20SYAHIRUL%20AMIN%20MEGAT%20ALI%20TDRA%20VOL%2014%20IGS%2018.pdf Megat Ali, Megat Syahirul Amin (2018) Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 14 (14). Institute of Graduate Studies, UiTM, Shah Alam.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Computers in education. Information technology Learning. Learning strategies
spellingShingle	Computers in education. Information technology Learning. Learning strategies Megat Ali, Megat Syahirul Amin Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali
description	The electroencephalogram is an effective approach for measuring brainwaves and has been widely used to study mental performance such as learning and intelligence. Conventional assessment methods are exposed to reliability issues which stems from cultural and language barriers. Alternative approach based on electroencephalogram has since been proposed; indicating correlation between resting brainwaves and learning styles. Validity of the findings however, was based on unconfirmed theories. Moreover, a systematic approach for learning style assessment based on brainwaves and advanced modelling technique as yet to be studied. Therefore, this research proposes an intelligent learning style classification model via brainwave features and artificial neural network. Eighty samples from various universities are segregated into four learning style groups based on Kolb's Learning Style Inventory. Twenty samples are identified as Divergers, twenty-two as Assimilators, twenty-one as Convergers and seventeen as Accommodators. Resting electroencephalogram is then recorded from the prefrontal region. Spectral centroid features from theta and alpha bands are then extracted for independent pattern analysis. Meanwhile, k-nearest neighbour is used for feature selection purposes. An intelligent learning style classification model is then constructed using spectral centroid features and multi-layered perceptron network. An independent dataset of fifty samples with varying levels of intelligence is used for a cross-relational mapping by the model. The pattern of features for each learning style group has shown correlation with the Neural Efficiency Hypothesis of intelligence. Subsequently, the fully developed model has attained excellent classification accuracy of 98.8% with mean squared error of 0.07. Moreover, the network has fulfilled all correlation requirements in classifying learning styles. The cross-relational analysis revealed that brighter individuals are predicted to be either Assimilative or Convergent. Meanwhile, the less brilliant ones are predicted to be either Divergent or Accommodative. Therefore, high level of intelligence is linked to excellent analytical skills, whereas low level of intelligence is associated with reliance on intuition rather than cognitive abilities. Conclusively, this thesis has proven that spectral centroid features from the resting brainwaves are suitable descriptors for characterising learning styles. The systematic approach established by the intelligent model provides an alternative for assessing the behaviour via electroencephalogram. Furthermore, the study has also confirmed that brainwaves from the prefrontal region are adequate for classification of learning styles.
format	Book Section
author	Megat Ali, Megat Syahirul Amin
author_facet	Megat Ali, Megat Syahirul Amin
author_sort	Megat Ali, Megat Syahirul Amin
title	Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_short	Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_full	Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_fullStr	Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_full_unstemmed	Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_sort	intelligent learning style classification model and crossrelational study with intelligence quotient / megat syahirul amin megat ali
publisher	Institute of Graduate Studies, UiTM
publishDate	2018
url	http://ir.uitm.edu.my/id/eprint/22224/ http://ir.uitm.edu.my/id/eprint/22224/1/ABS_MEGAT%20SYAHIRUL%20AMIN%20MEGAT%20ALI%20TDRA%20VOL%2014%20IGS%2018.pdf
first_indexed	2023-09-18T23:08:12Z
last_indexed	2023-09-18T23:08:12Z
_version_	1777418625771110400

Intelligent learning style classification model and crossrelational study with intelligence quotient / Megat Syahirul Amin Megat Ali

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