Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accur...

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Autores principales:	Boran Sekeroglu, Rahib Abiyev, Ahmet Ilhan, Murat Arslan, John Bush Idoko
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	student performance prediction AI machine learning deep learning education in-term 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/10e9af06eb134a51901561ed1ec908d2
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spelling	oai:doaj.org-article:10e9af06eb134a51901561ed1ec908d22021-11-25T16:40:28ZSystematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies10.3390/app1122109072076-3417https://doaj.org/article/10e9af06eb134a51901561ed1ec908d22021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10907https://doaj.org/toc/2076-3417Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.Boran SekerogluRahib AbiyevAhmet IlhanMurat ArslanJohn Bush IdokoMDPI AGarticlestudent performance predictionAImachine learningdeep learningeducationin-termTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10907, p 10907 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	student performance prediction AI machine learning deep learning education in-term Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	student performance prediction AI machine learning deep learning education in-term Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Boran Sekeroglu Rahib Abiyev Ahmet Ilhan Murat Arslan John Bush Idoko Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies
description	Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.
format	article
author	Boran Sekeroglu Rahib Abiyev Ahmet Ilhan Murat Arslan John Bush Idoko
author_facet	Boran Sekeroglu Rahib Abiyev Ahmet Ilhan Murat Arslan John Bush Idoko
author_sort	Boran Sekeroglu
title	Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies
title_short	Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies
title_full	Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies
title_fullStr	Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies
title_full_unstemmed	Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies
title_sort	systematic literature review on machine learning and student performance prediction: critical gaps and possible remedies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/10e9af06eb134a51901561ed1ec908d2
work_keys_str_mv	AT boransekeroglu systematicliteraturereviewonmachinelearningandstudentperformancepredictioncriticalgapsandpossibleremedies AT rahibabiyev systematicliteraturereviewonmachinelearningandstudentperformancepredictioncriticalgapsandpossibleremedies AT ahmetilhan systematicliteraturereviewonmachinelearningandstudentperformancepredictioncriticalgapsandpossibleremedies AT muratarslan systematicliteraturereviewonmachinelearningandstudentperformancepredictioncriticalgapsandpossibleremedies AT johnbushidoko systematicliteraturereviewonmachinelearningandstudentperformancepredictioncriticalgapsandpossibleremedies
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Systematic Literature Review on Machine Learning and Student Performance Prediction: Critical Gaps and Possible Remedies

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