Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad

This thesis describes the development of Electrical Engineering students' performance prediction model using Artificial Neural Network (ANN) based on SIMS data from three generations of Matriculation and Diploma students. It was observed that there was a certain pattern or trend between the str...

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Main Author:	Mohd Arsad, Pauziah
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Performance Competence Academic achievement Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/18575/1/18575.pdf
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id	my-uitm-ir.18575
record_format	uketd_dc
spelling	my-uitm-ir.185752022-05-16T23:52:14Z Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad 2016-01 Mohd Arsad, Pauziah Performance. Competence. Academic achievement Neural networks (Computer science) This thesis describes the development of Electrical Engineering students' performance prediction model using Artificial Neural Network (ANN) based on SIMS data from three generations of Matriculation and Diploma students. It was observed that there was a certain pattern or trend between the strong ability students and the weaker ones in terms of performance. The strong ability students managed to graduate steadily with high CGPA upon graduation, while the weaker ones tend to waver and finally graduate with minimum CGPA or even extended for another one or two semesters to complete the required credit hours. The Grade Points (GP) of fundamental subjects attempted at semester one was used as inputs to the developed Neural Network Students' Performance Prediction Model (NNSPPM) to predict the output which is CGPA8 upon graduation. The fundamental subjects strongly influenced the overall performance of students. The NNSPPM was then tested with another set of input data consisting GP of subjects at semester three to see the predicted output. The NNSPPM was further validated with a different set of data, namely Diploma students taking the same subjects at semester three, sitting the same set of examination questions as that of Matriculation students. The trend and pattern of predicted output seemed to hold true for all three different cases. It was found that at lower CGPA8, the predicted output is higher than the actual CGPA8; while at high CGPA8, the predicted is lower than the actual CGPA8 for the Matriculation and Diploma students. Subsequently a second method, Linear Regression was used to predict the final CGPA. GP of subjects scored by students form the independent while the CGPA8 formed the dependent variable. However, when the coefficient of Correlation R was compared between the two methods, NN method was found to be more accurate in terms of prediction. The Mean Square Error or Residual is almost the same in both methods. Thus the fundamental subjects at semester one or three have direct impact on CGPA8. The fundamental subjects strongly influenced performance of students. By using the prediction model, strategic intervention by the academic advisors can be offered to the underachieving students once detected by the model. 2016-01 Thesis https://ir.uitm.edu.my/id/eprint/18575/ https://ir.uitm.edu.my/id/eprint/18575/1/18575.pdf text en public phd doctoral Universiti Teknologi MARA Faculty of Electrical Engineering Buniyamin, Norlida
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Buniyamin, Norlida
topic	Performance Competence Academic achievement Neural networks (Computer science)
spellingShingle	Performance Competence Academic achievement Neural networks (Computer science) Mohd Arsad, Pauziah Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad
description	This thesis describes the development of Electrical Engineering students' performance prediction model using Artificial Neural Network (ANN) based on SIMS data from three generations of Matriculation and Diploma students. It was observed that there was a certain pattern or trend between the strong ability students and the weaker ones in terms of performance. The strong ability students managed to graduate steadily with high CGPA upon graduation, while the weaker ones tend to waver and finally graduate with minimum CGPA or even extended for another one or two semesters to complete the required credit hours. The Grade Points (GP) of fundamental subjects attempted at semester one was used as inputs to the developed Neural Network Students' Performance Prediction Model (NNSPPM) to predict the output which is CGPA8 upon graduation. The fundamental subjects strongly influenced the overall performance of students. The NNSPPM was then tested with another set of input data consisting GP of subjects at semester three to see the predicted output. The NNSPPM was further validated with a different set of data, namely Diploma students taking the same subjects at semester three, sitting the same set of examination questions as that of Matriculation students. The trend and pattern of predicted output seemed to hold true for all three different cases. It was found that at lower CGPA8, the predicted output is higher than the actual CGPA8; while at high CGPA8, the predicted is lower than the actual CGPA8 for the Matriculation and Diploma students. Subsequently a second method, Linear Regression was used to predict the final CGPA. GP of subjects scored by students form the independent while the CGPA8 formed the dependent variable. However, when the coefficient of Correlation R was compared between the two methods, NN method was found to be more accurate in terms of prediction. The Mean Square Error or Residual is almost the same in both methods. Thus the fundamental subjects at semester one or three have direct impact on CGPA8. The fundamental subjects strongly influenced performance of students. By using the prediction model, strategic intervention by the academic advisors can be offered to the underachieving students once detected by the model.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohd Arsad, Pauziah
author_facet	Mohd Arsad, Pauziah
author_sort	Mohd Arsad, Pauziah
title	Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad
title_short	Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad
title_full	Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad
title_fullStr	Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad
title_full_unstemmed	Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad
title_sort	prediction of engineering students' academic performance using neural network and linear regression / pauziah mohd arsad
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	https://ir.uitm.edu.my/id/eprint/18575/1/18575.pdf
_version_	1783733695504449536

Prediction of engineering students' academic performance using neural network and linear regression / Pauziah Mohd Arsad

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