Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance

The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experi ence some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of fail ure and low Grade Poi...

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Main Authors:	Ajiboye, Adeleke Raheem, Ruzaini, Abdullah Arshah, Qin, Hongwu
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/14580/ http://umpir.ump.edu.my/id/eprint/14580/6/fskkp-2015-ruzaini-Using%20an%20enhanced%20feed-forward%20neural.pdf

id	ump-14580
recordtype	eprints
spelling	ump-145802018-05-18T01:35:51Z http://umpir.ump.edu.my/id/eprint/14580/ Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Qin, Hongwu QA76 Computer software The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experi ence some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of fail ure and low Grade Point Average (GPA). Computing the earlier academjc achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-infonned of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network fo r the creation of a network model to predict the students' performance based on their historical data is proposed. In the course of experimentations with Matlab software, two network models are crea ted using the existing and enhanced feed-forward neural network techniques. The abiliry of these models to generalize is measured using simulation methods. The enhanced network model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supponed financially in the fom1 of scholarship; while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14580/6/fskkp-2015-ruzaini-Using%20an%20enhanced%20feed-forward%20neural.pdf Ajiboye, Adeleke Raheem and Ruzaini, Abdullah Arshah and Qin, Hongwu (2015) Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance. In: Proceedings of 3rd International Conference on Computer Science and Data Mining (ICCSDM 2015), 20-21 May 2015 , Dubai, UAE. pp. 22-27..
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Qin, Hongwu Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
description	The newly admitted students for the undergraduate programmes in the institutions of higher learning sometimes experi ence some academic adjustment that is associated with stress; many factors have been attributed to this, which most times, results in the high percentage of fail ure and low Grade Point Average (GPA). Computing the earlier academjc achievements for these sets of students would make one to be abreast of their level of knowledge academically, in order to be well-infonned of their areas of weakness and strength. In this paper, an enhancement of Feed-forward Neural Network fo r the creation of a network model to predict the students' performance based on their historical data is proposed. In the course of experimentations with Matlab software, two network models are crea ted using the existing and enhanced feed-forward neural network techniques. The abiliry of these models to generalize is measured using simulation methods. The enhanced network model consistently shows a high degree of accuracy and predicts well. The performance of students predicted as outstanding, can also be supponed financially in the fom1 of scholarship; while those that are found to be academically weak can be encouraged and rightly counseled at the early stage of their studies.
format	Conference or Workshop Item
author	Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Qin, Hongwu
author_facet	Ajiboye, Adeleke Raheem Ruzaini, Abdullah Arshah Qin, Hongwu
author_sort	Ajiboye, Adeleke Raheem
title	Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
title_short	Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
title_full	Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
title_fullStr	Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
title_full_unstemmed	Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance
title_sort	using an enhanced feed-forward neural network technique for prediction of students' performance
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/14580/ http://umpir.ump.edu.my/id/eprint/14580/6/fskkp-2015-ruzaini-Using%20an%20enhanced%20feed-forward%20neural.pdf
first_indexed	2023-09-18T22:18:30Z
last_indexed	2023-09-18T22:18:30Z
_version_	1777415498375364608

Using an Enhanced Feed-Forward Neural Network Technique for Prediction of Students' Performance

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