RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

Predicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify fa...

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Autores principales:	Wen Xiao, Ping Ji, Juan Hu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/480056522eea49248668e07b7d0f283e
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id	oai:doaj.org-article:480056522eea49248668e07b7d0f283e
record_format	dspace
spelling	oai:doaj.org-article:480056522eea49248668e07b7d0f283e2021-11-22T01:10:52ZRnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance1875-919X10.1155/2021/1670593https://doaj.org/article/480056522eea49248668e07b7d0f283e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1670593https://doaj.org/toc/1875-919XPredicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students’ performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students’ performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students’ performance with commonly used classifiers about 10% and improve the precision of predicting students’ academic failure by up to 45%.Wen XiaoPing JiJuan HuHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Wen Xiao Ping Ji Juan Hu RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance
description	Predicting students’ performance is one of the most concerned issues in education data mining (EDM), which has received more and more attentions. Feature selection is the key step to build prediction model of students’ performance, which can improve the accuracy of prediction and help to identify factors that have significant impact on students’ performance. In this paper, a hybrid feature selection method named rank and heuristic (RnkHEU) was proposed. This novel feature selection method generates the set of candidate features by scoring and ranking firstly and then uses heuristic method to generate the final results. The experimental results show that the four major evaluation criteria have similar performance in predicting students’ performance, and the heuristic search strategy can significantly improve the accuracy of prediction compared with forward search method. Because the proposed RnkHEU integrates ranking-based forward and heuristic search, it can further improve the accuracy of predicting students’ performance with commonly used classifiers about 10% and improve the precision of predicting students’ academic failure by up to 45%.
format	article
author	Wen Xiao Ping Ji Juan Hu
author_facet	Wen Xiao Ping Ji Juan Hu
author_sort	Wen Xiao
title	RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance
title_short	RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance
title_full	RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance
title_fullStr	RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance
title_full_unstemmed	RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance
title_sort	rnkheu: a hybrid feature selection method for predicting students’ performance
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/480056522eea49248668e07b7d0f283e
work_keys_str_mv	AT wenxiao rnkheuahybridfeatureselectionmethodforpredictingstudentsperformance AT pingji rnkheuahybridfeatureselectionmethodforpredictingstudentsperformance AT juanhu rnkheuahybridfeatureselectionmethodforpredictingstudentsperformance
_version_	1718418336123977728

RnkHEU: A Hybrid Feature Selection Method for Predicting Students’ Performance

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