An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance

Feed-forward Neural Networks, is a multilayer perceptron and a network structure capable of modelling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks. But sometimes, it has challenges of over-fitting, especially w...

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Main Author:	Raheem, Ajiboye Adeleke
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Q Science (General) T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/15251/1/An%20enhanced%20feed-forward%20neural%20networks%20and%20a%20rule-based%20algorithm%20for%20predictive%20modelling%20of%20students%27%20academic%20performance.pdf
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id	my-ump-ir.15251
record_format	uketd_dc
spelling	my-ump-ir.152512023-03-02T04:33:27Z An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance 2016-02 Raheem, Ajiboye Adeleke Q Science (General) T Technology (General) Feed-forward Neural Networks, is a multilayer perceptron and a network structure capable of modelling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks. But sometimes, it has challenges of over-fitting, especially when fitting models from massive data of varied data points. This necessitates its enhancement in order to strengthen its performance. Such enhancement would ensure a predictive network model that can generalize well with a set of untrained data. In this research, in order to alleviate the possibility of over-fitting in a network predictive model, a dynamic partitioning of the dataset is proposed. Also, for a more efficient exploration of students‟ data collected for this research, a Rule-Based Algorithm is proposed and implemented. The predictive models emanated from the two approaches were evaluated in order to validate their effectiveness. The enhancement done to the Feed-forward Neural Networks (FNN) in the first approach, ensure partitioning of the dataset that is based on the size of the data available for creating the model. The evaluation carried out on the Enhanced Feed-forward Neural Network (EFNN) models show that, there is a decrease in error from 0.261 to 0.029. Similarly, another set of 2000 students‟ data is trained, the error recorded when the network model is simulated with untrained 500 data show that, there is a reduction in error from 0.0095 to 0.00033. Most of the training performance generated from the network models created also shows that, the EFNN has lower errors and converge faster. The implementation of the rule-based algorithm proposed in the second approach, shows outputs that are consistently accurate. Its efficiency is compared to some existing techniques reported in the literature for the predictive modelling of students‟ academic performance. Findings from the comparison show that, the proposed RBA explores students‟ data much better. It can also serve as an alternative algorithm to the use of machine learning techniques in the exploration of students‟ data for prediction purposes. 2016-02 Thesis http://umpir.ump.edu.my/id/eprint/15251/ http://umpir.ump.edu.my/id/eprint/15251/1/An%20enhanced%20feed-forward%20neural%20networks%20and%20a%20rule-based%20algorithm%20for%20predictive%20modelling%20of%20students%27%20academic%20performance.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computer Systems and Software Engineering Abdullah Arshah, Ruzaini
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Abdullah Arshah, Ruzaini
topic	Q Science (General) T Technology (General)
spellingShingle	Q Science (General) T Technology (General) Raheem, Ajiboye Adeleke An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
description	Feed-forward Neural Networks, is a multilayer perceptron and a network structure capable of modelling the class prediction as a nonlinear combination of the inputs. The network has proven its suitability in solving several complex tasks. But sometimes, it has challenges of over-fitting, especially when fitting models from massive data of varied data points. This necessitates its enhancement in order to strengthen its performance. Such enhancement would ensure a predictive network model that can generalize well with a set of untrained data. In this research, in order to alleviate the possibility of over-fitting in a network predictive model, a dynamic partitioning of the dataset is proposed. Also, for a more efficient exploration of students‟ data collected for this research, a Rule-Based Algorithm is proposed and implemented. The predictive models emanated from the two approaches were evaluated in order to validate their effectiveness. The enhancement done to the Feed-forward Neural Networks (FNN) in the first approach, ensure partitioning of the dataset that is based on the size of the data available for creating the model. The evaluation carried out on the Enhanced Feed-forward Neural Network (EFNN) models show that, there is a decrease in error from 0.261 to 0.029. Similarly, another set of 2000 students‟ data is trained, the error recorded when the network model is simulated with untrained 500 data show that, there is a reduction in error from 0.0095 to 0.00033. Most of the training performance generated from the network models created also shows that, the EFNN has lower errors and converge faster. The implementation of the rule-based algorithm proposed in the second approach, shows outputs that are consistently accurate. Its efficiency is compared to some existing techniques reported in the literature for the predictive modelling of students‟ academic performance. Findings from the comparison show that, the proposed RBA explores students‟ data much better. It can also serve as an alternative algorithm to the use of machine learning techniques in the exploration of students‟ data for prediction purposes.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Raheem, Ajiboye Adeleke
author_facet	Raheem, Ajiboye Adeleke
author_sort	Raheem, Ajiboye Adeleke
title	An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
title_short	An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
title_full	An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
title_fullStr	An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
title_full_unstemmed	An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
title_sort	enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computer Systems and Software Engineering
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/15251/1/An%20enhanced%20feed-forward%20neural%20networks%20and%20a%20rule-based%20algorithm%20for%20predictive%20modelling%20of%20students%27%20academic%20performance.pdf
_version_	1783731995437694976

An enhanced feed-forward neural networks and a rule-based algorithm for predictive modelling of students' academic performance

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