Mining student information system records to predict students’ academic performance

Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset wa...

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Main Authors:	Saa, Amjad Abu, Al-Emran, Mostafa, Shaalan, Khaled
Format:	Conference or Workshop Item
Language:	English
Published:	Springer Verlag 2020
Subjects:	LB2300 Higher Education QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/24977/ http://umpir.ump.edu.my/id/eprint/24977/ http://umpir.ump.edu.my/id/eprint/24977/1/Mining%20student%20information%20system%20records%20to%20predict%20students%E2%80%99.pdf

id	ump-24977
recordtype	eprints
spelling	ump-249772019-11-28T07:40:50Z http://umpir.ump.edu.my/id/eprint/24977/ Mining student information system records to predict students’ academic performance Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled LB2300 Higher Education QA76 Computer software Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance. Springer Verlag 2020 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24977/1/Mining%20student%20information%20system%20records%20to%20predict%20students%E2%80%99.pdf Saa, Amjad Abu and Al-Emran, Mostafa and Shaalan, Khaled (2020) Mining student information system records to predict students’ academic performance. In: 4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019, 28 - 30 March 2019 , Cairo, Egypt. pp. 229-239., 921. ISSN 2194-5357 ISBN 978-3-030-14117-2 (Print); 978-3-030-14118-9 (Online) https://doi.org/10.1007/978-3-030-14118-9_23
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	LB2300 Higher Education QA76 Computer software
spellingShingle	LB2300 Higher Education QA76 Computer software Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled Mining student information system records to predict students’ academic performance
description	Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance.
format	Conference or Workshop Item
author	Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled
author_facet	Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled
author_sort	Saa, Amjad Abu
title	Mining student information system records to predict students’ academic performance
title_short	Mining student information system records to predict students’ academic performance
title_full	Mining student information system records to predict students’ academic performance
title_fullStr	Mining student information system records to predict students’ academic performance
title_full_unstemmed	Mining student information system records to predict students’ academic performance
title_sort	mining student information system records to predict students’ academic performance
publisher	Springer Verlag
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/24977/ http://umpir.ump.edu.my/id/eprint/24977/ http://umpir.ump.edu.my/id/eprint/24977/1/Mining%20student%20information%20system%20records%20to%20predict%20students%E2%80%99.pdf
first_indexed	2023-09-18T22:38:06Z
last_indexed	2023-09-18T22:38:06Z
_version_	1777416731651735552

Mining student information system records to predict students’ academic performance

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