Data mining approach to the identification of at-risk students

In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educa...

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Main Authors:	HO, Li Chin, SHIM, Kyong Jin
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2018
Subjects:	educational data mining at-risk students learning management systems learning analytics MITB student Databases and Information Systems Educational Assessment, Evaluation, and Research Numerical Analysis and Scientific Computing
Online Access:	https://ink.library.smu.edu.sg/sis_research/4339 https://ink.library.smu.edu.sg/context/sis_research/article/5342/viewcontent/DataMining_At_Risk_Students_2018_av.pdf
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-5342
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spelling	sg-smu-ink.sis_research-53422021-06-07T06:01:05Z Data mining approach to the identification of at-risk students HO, Li Chin SHIM, Kyong Jin In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro. 2018-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4339 info:doi/10.1109/BigData.2018.8622495 https://ink.library.smu.edu.sg/context/sis_research/article/5342/viewcontent/DataMining_At_Risk_Students_2018_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University educational data mining at-risk students learning management systems learning analytics MITB student Databases and Information Systems Educational Assessment, Evaluation, and Research Numerical Analysis and Scientific Computing
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	educational data mining at-risk students learning management systems learning analytics MITB student Databases and Information Systems Educational Assessment, Evaluation, and Research Numerical Analysis and Scientific Computing
spellingShingle	educational data mining at-risk students learning management systems learning analytics MITB student Databases and Information Systems Educational Assessment, Evaluation, and Research Numerical Analysis and Scientific Computing HO, Li Chin SHIM, Kyong Jin Data mining approach to the identification of at-risk students
description	In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro.
format	text
author	HO, Li Chin SHIM, Kyong Jin
author_facet	HO, Li Chin SHIM, Kyong Jin
author_sort	HO, Li Chin
title	Data mining approach to the identification of at-risk students
title_short	Data mining approach to the identification of at-risk students
title_full	Data mining approach to the identification of at-risk students
title_fullStr	Data mining approach to the identification of at-risk students
title_full_unstemmed	Data mining approach to the identification of at-risk students
title_sort	data mining approach to the identification of at-risk students
publisher	Institutional Knowledge at Singapore Management University
publishDate	2018
url	https://ink.library.smu.edu.sg/sis_research/4339 https://ink.library.smu.edu.sg/context/sis_research/article/5342/viewcontent/DataMining_At_Risk_Students_2018_av.pdf
_version_	1770574659448733696

Data mining approach to the identification of at-risk students

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