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Intelligent learning style classification model and cross-relational 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: Thesis
Language:English
Published: 2018
Subjects:
Technical education. Technical schools
Online Access:https://ir.uitm.edu.my/id/eprint/27831/2/27831.pdf
https://ir.uitm.edu.my/id/eprint/27831/
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spelling my.uitm.ir.278312024-05-03T08:13:13Z https://ir.uitm.edu.my/id/eprint/27831/ Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali Megat Ali, Megat Syahirul Amin Technical education. Technical schools 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. 2018 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/27831/2/27831.pdf Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali. (2018) PhD thesis, thesis, Universiti Teknologi MARA. <http://terminalib.uitm.edu.my/27831.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Technical education. Technical schools
spellingShingle Technical education. Technical schools
Megat Ali, Megat Syahirul Amin
Intelligent learning style classification model and cross-relational 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 Thesis
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 cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_short Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_full Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_fullStr Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_full_unstemmed Intelligent learning style classification model and cross-relational study with intelligence quotient / Megat Syahirul Amin Megat Ali
title_sort intelligent learning style classification model and cross-relational study with intelligence quotient / megat syahirul amin megat ali
publishDate 2018
url https://ir.uitm.edu.my/id/eprint/27831/2/27831.pdf
https://ir.uitm.edu.my/id/eprint/27831/
_version_ 1800100540455059456
score 13.222552

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