Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level m...

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Autores principales:	Latif Sohaib, XianWen Fang, Wang Li-li
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	neural network real-world data mining decision tree machine learning Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/ca51c15e1bb2446992f6ef9429e0904e
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id	oai:doaj.org-article:ca51c15e1bb2446992f6ef9429e0904e
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spelling	oai:doaj.org-article:ca51c15e1bb2446992f6ef9429e0904e2021-12-05T14:10:51ZIntelligent decision support system approach for predicting the performance of students based on three-level machine learning technique2191-026X10.1515/jisys-2020-0065https://doaj.org/article/ca51c15e1bb2446992f6ef9429e0904e2021-05-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0065https://doaj.org/toc/2191-026XIn this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared (χ)\left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems.Latif SohaibXianWen FangWang Li-liDe Gruyterarticleneural networkreal-world data miningdecision treemachine learningScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 739-749 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	neural network real-world data mining decision tree machine learning Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	neural network real-world data mining decision tree machine learning Science Q Electronic computers. Computer science QA75.5-76.95 Latif Sohaib XianWen Fang Wang Li-li Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
description	In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared (χ)\left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems.
format	article
author	Latif Sohaib XianWen Fang Wang Li-li
author_facet	Latif Sohaib XianWen Fang Wang Li-li
author_sort	Latif Sohaib
title	Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
title_short	Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
title_full	Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
title_fullStr	Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
title_full_unstemmed	Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
title_sort	intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/ca51c15e1bb2446992f6ef9429e0904e
work_keys_str_mv	AT latifsohaib intelligentdecisionsupportsystemapproachforpredictingtheperformanceofstudentsbasedonthreelevelmachinelearningtechnique AT xianwenfang intelligentdecisionsupportsystemapproachforpredictingtheperformanceofstudentsbasedonthreelevelmachinelearningtechnique AT wanglili intelligentdecisionsupportsystemapproachforpredictingtheperformanceofstudentsbasedonthreelevelmachinelearningtechnique
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Intelligent decision support system approach for predicting the performance of students based on three-level machine learning technique

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