IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE

Classification under supervision is the most common job that performed by machine learning. However, most Educators were worried about the rising evidence of student academic failures in university education. So, this study presents a supervised classification strategy of machine learning algorithm...

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Autores principales:	Nibras Z. Salih, Walaa Khalaf
Formato:	article
Lenguaje:	AR EN
Publicado:	Mustansiriyah University/College of Engineering 2021
Materias:	leave one out cross-validation (loocv) receiver operating characteristics (roc) precision-recall curve (prc) synthetic minority oversampling technique (smote) Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/8e09be7ccb774984a1c20ac4c752b82b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8e09be7ccb774984a1c20ac4c752b82b
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spelling	oai:doaj.org-article:8e09be7ccb774984a1c20ac4c752b82b2021-11-10T10:38:15ZIMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE 10.31272/jeasd.25.6.62520-09172520-0925https://doaj.org/article/8e09be7ccb774984a1c20ac4c752b82b2021-11-01T00:00:00Zhttps://www.iasj.net/iasj/download/c7e7baad02363835https://doaj.org/toc/2520-0917https://doaj.org/toc/2520-0925Classification under supervision is the most common job that performed by machine learning. However, most Educators were worried about the rising evidence of student academic failures in university education. So, this study presents a supervised classification strategy of machine learning algorithm using an actual dataset contains 44 students, fourteen attributes for three previous academic years. We have proposed features that show the relationship among three main subjects which are, calculus, mathematical analysis, and control system in the education course. The objective of this study is to identify the student’s failure in the control system subject and to enhance his performance by Multilayer Perceptron (MLP) algorithm. The dataset is unbalanced, which causes overfitting of the results. Synthetic Minority Oversampling Technique has applied to a dataset for obtaining balance dataset using Weka tool. Several standard metrics used to evaluate the classifier results. Therefore, the suitable results occurred after applying SMOTE with an accuracy of 76.9%.Nibras Z. SalihWalaa KhalafMustansiriyah University/College of Engineeringarticleleave one out cross-validation (loocv)receiver operating characteristics (roc)precision-recall curve (prc)synthetic minority oversampling technique (smote)Engineering (General). Civil engineering (General)TA1-2040ARENJournal of Engineering and Sustainable Development, Vol 25, Iss 6, Pp 56-64 (2021)
institution	DOAJ
collection	DOAJ
language	AR EN
topic	leave one out cross-validation (loocv) receiver operating characteristics (roc) precision-recall curve (prc) synthetic minority oversampling technique (smote) Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	leave one out cross-validation (loocv) receiver operating characteristics (roc) precision-recall curve (prc) synthetic minority oversampling technique (smote) Engineering (General). Civil engineering (General) TA1-2040 Nibras Z. Salih Walaa Khalaf IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
description	Classification under supervision is the most common job that performed by machine learning. However, most Educators were worried about the rising evidence of student academic failures in university education. So, this study presents a supervised classification strategy of machine learning algorithm using an actual dataset contains 44 students, fourteen attributes for three previous academic years. We have proposed features that show the relationship among three main subjects which are, calculus, mathematical analysis, and control system in the education course. The objective of this study is to identify the student’s failure in the control system subject and to enhance his performance by Multilayer Perceptron (MLP) algorithm. The dataset is unbalanced, which causes overfitting of the results. Synthetic Minority Oversampling Technique has applied to a dataset for obtaining balance dataset using Weka tool. Several standard metrics used to evaluate the classifier results. Therefore, the suitable results occurred after applying SMOTE with an accuracy of 76.9%.
format	article
author	Nibras Z. Salih Walaa Khalaf
author_facet	Nibras Z. Salih Walaa Khalaf
author_sort	Nibras Z. Salih
title	IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
title_short	IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
title_full	IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
title_fullStr	IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
title_full_unstemmed	IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE
title_sort	improving students performance prediction using machine learning and synthetic minority oversampling technique
publisher	Mustansiriyah University/College of Engineering
publishDate	2021
url	https://doaj.org/article/8e09be7ccb774984a1c20ac4c752b82b
work_keys_str_mv	AT nibraszsalih improvingstudentsperformancepredictionusingmachinelearningandsyntheticminorityoversamplingtechnique AT walaakhalaf improvingstudentsperformancepredictionusingmachinelearningandsyntheticminorityoversamplingtechnique
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IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE

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