Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review

Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of thei...

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Autores principales:	Prasanalakshmi Balaji, Salem Alelyani, Ayman Qahmash, Mohamed Mohana
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	educational mining machine learning artificial intelligence decision support systems systematic literature review Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/78d78c1c25a046559407ed1e40ccea1e
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spelling	oai:doaj.org-article:78d78c1c25a046559407ed1e40ccea1e2021-11-11T15:05:30ZContributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review10.3390/app1121100072076-3417https://doaj.org/article/78d78c1c25a046559407ed1e40ccea1e2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10007https://doaj.org/toc/2076-3417Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of their course and for the students in deciding to choose a suitable course for them based on their performances in previous exams and other behavioral features. In this study, a systematic literature review is performed to extract the algorithms and the features that have been used in the prediction studies. Based on the search criteria, 2700 articles were initially considered. Using specified inclusion and exclusion criteria, quality scores were provided, and up to 56 articles were filtered for further analysis. The utmost care was taken in studying the features utilized, database used, algorithms implemented, and the future directions as recommended by researchers. The features were classified as demographic, academic, and behavioral features, and finally, only 34 articles with these features were finalized, whose details of study are provided. Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability to predict the students’ performance based on specified features as categorized and can be used by students as well as academic institutions. A specific machine learning model identification for the purpose of student academic performance prediction would not be feasible, since each paper taken for review involves different datasets and does not include benchmark datasets. However, the application of the machine learning techniques in educational mining is still limited, and a greater number of studies should be carried out in order to obtain well-formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.Prasanalakshmi BalajiSalem AlelyaniAyman QahmashMohamed MohanaMDPI AGarticleeducational miningmachine learningartificial intelligencedecision support systemssystematic literature reviewTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10007, p 10007 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	educational mining machine learning artificial intelligence decision support systems systematic literature review Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	educational mining machine learning artificial intelligence decision support systems systematic literature review Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Prasanalakshmi Balaji Salem Alelyani Ayman Qahmash Mohamed Mohana Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
description	Machine learning is emerging nowadays as an important tool for decision support in many areas of research. In the field of education, both educational organizations and students are the target beneficiaries. It facilitates the educational sector in predicting the student’s outcome at the end of their course and for the students in deciding to choose a suitable course for them based on their performances in previous exams and other behavioral features. In this study, a systematic literature review is performed to extract the algorithms and the features that have been used in the prediction studies. Based on the search criteria, 2700 articles were initially considered. Using specified inclusion and exclusion criteria, quality scores were provided, and up to 56 articles were filtered for further analysis. The utmost care was taken in studying the features utilized, database used, algorithms implemented, and the future directions as recommended by researchers. The features were classified as demographic, academic, and behavioral features, and finally, only 34 articles with these features were finalized, whose details of study are provided. Based on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability to predict the students’ performance based on specified features as categorized and can be used by students as well as academic institutions. A specific machine learning model identification for the purpose of student academic performance prediction would not be feasible, since each paper taken for review involves different datasets and does not include benchmark datasets. However, the application of the machine learning techniques in educational mining is still limited, and a greater number of studies should be carried out in order to obtain well-formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.
format	article
author	Prasanalakshmi Balaji Salem Alelyani Ayman Qahmash Mohamed Mohana
author_facet	Prasanalakshmi Balaji Salem Alelyani Ayman Qahmash Mohamed Mohana
author_sort	Prasanalakshmi Balaji
title	Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
title_short	Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
title_full	Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
title_fullStr	Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
title_full_unstemmed	Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review
title_sort	contributions of machine learning models towards student academic performance prediction: a systematic review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/78d78c1c25a046559407ed1e40ccea1e
work_keys_str_mv	AT prasanalakshmibalaji contributionsofmachinelearningmodelstowardsstudentacademicperformancepredictionasystematicreview AT salemalelyani contributionsofmachinelearningmodelstowardsstudentacademicperformancepredictionasystematicreview AT aymanqahmash contributionsofmachinelearningmodelstowardsstudentacademicperformancepredictionasystematicreview AT mohamedmohana contributionsofmachinelearningmodelstowardsstudentacademicperformancepredictionasystematicreview
_version_	1718437150801788928

Contributions of Machine Learning Models towards Student Academic Performance Prediction: A Systematic Review

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