Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

Abstract Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even more important to identify these students and make i...

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Autores principales:	Halit Karalar, Ceyhun Kapucu, Hüseyin Gürüler
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Predicting student performance Predicting student at risk Ensemble learning model Educational data mining Distance learning COVID-19 pandemic Special aspects of education LC8-6691 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/e89539dd763a4f558d88492d3563f365
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spelling	oai:doaj.org-article:e89539dd763a4f558d88492d3563f3652021-12-05T12:08:57ZPredicting students at risk of academic failure using ensemble model during pandemic in a distance learning system10.1186/s41239-021-00300-y2365-9440https://doaj.org/article/e89539dd763a4f558d88492d3563f3652021-12-01T00:00:00Zhttps://doi.org/10.1186/s41239-021-00300-yhttps://doaj.org/toc/2365-9440Abstract Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even more important to identify these students and make instructional interventions to avoid leaving them behind. This goal can be achieved by new data mining techniques and machine learning methods. This study took both the synchronous and asynchronous activity characteristics of students into account to identify students at risk of academic failure during the pandemic. Additionally, this study proposes an optimal ensemble model predicting students at risk using a combination of relevant machine learning algorithms. Performances of over two thousand university students were predicted with an ensemble model in terms of gender, degree, number of downloaded lecture notes and course materials, total time spent in online sessions, number of attendances, and quiz score. Asynchronous learning activities were found more determinant than synchronous ones. The proposed ensemble model made a good prediction with a specificity of 90.34%. Thus, practitioners are suggested to monitor and organize training activities accordingly.Halit KaralarCeyhun KapucuHüseyin GürülerSpringerOpenarticlePredicting student performancePredicting student at riskEnsemble learning modelEducational data miningDistance learningCOVID-19 pandemicSpecial aspects of educationLC8-6691Information technologyT58.5-58.64ENInternational Journal of Educational Technology in Higher Education, Vol 18, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Predicting student performance Predicting student at risk Ensemble learning model Educational data mining Distance learning COVID-19 pandemic Special aspects of education LC8-6691 Information technology T58.5-58.64
spellingShingle	Predicting student performance Predicting student at risk Ensemble learning model Educational data mining Distance learning COVID-19 pandemic Special aspects of education LC8-6691 Information technology T58.5-58.64 Halit Karalar Ceyhun Kapucu Hüseyin Gürüler Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
description	Abstract Predicting students at risk of academic failure is valuable for higher education institutions to improve student performance. During the pandemic, with the transition to compulsory distance learning in higher education, it has become even more important to identify these students and make instructional interventions to avoid leaving them behind. This goal can be achieved by new data mining techniques and machine learning methods. This study took both the synchronous and asynchronous activity characteristics of students into account to identify students at risk of academic failure during the pandemic. Additionally, this study proposes an optimal ensemble model predicting students at risk using a combination of relevant machine learning algorithms. Performances of over two thousand university students were predicted with an ensemble model in terms of gender, degree, number of downloaded lecture notes and course materials, total time spent in online sessions, number of attendances, and quiz score. Asynchronous learning activities were found more determinant than synchronous ones. The proposed ensemble model made a good prediction with a specificity of 90.34%. Thus, practitioners are suggested to monitor and organize training activities accordingly.
format	article
author	Halit Karalar Ceyhun Kapucu Hüseyin Gürüler
author_facet	Halit Karalar Ceyhun Kapucu Hüseyin Gürüler
author_sort	Halit Karalar
title	Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
title_short	Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
title_full	Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
title_fullStr	Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
title_full_unstemmed	Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
title_sort	predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/e89539dd763a4f558d88492d3563f365
work_keys_str_mv	AT halitkaralar predictingstudentsatriskofacademicfailureusingensemblemodelduringpandemicinadistancelearningsystem AT ceyhunkapucu predictingstudentsatriskofacademicfailureusingensemblemodelduringpandemicinadistancelearningsystem AT huseyinguruler predictingstudentsatriskofacademicfailureusingensemblemodelduringpandemicinadistancelearningsystem
_version_	1718372226800025600

Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system

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