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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| Format: | Conference or Workshop Item |
| Language: | en |
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Springer Verlag
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/24977/1/Mining%20student%20information%20system%20records%20to%20predict%20students%E2%80%99.pdf http://umpir.ump.edu.my/id/eprint/24977/ https://doi.org/10.1007/978-3-030-14118-9_23 |
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| author | Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled |
| author_facet | Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled |
| author_sort | Saa, Amjad Abu |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.ump.umpir.24977 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2020 |
| publisher | Springer Verlag |
| record_format | eprints |
| spelling | my.ump.umpir.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) (Published) https://doi.org/10.1007/978-3-030-14118-9_23 |
| 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 |
| title | 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_short | Mining student information system records to predict students’ academic performance |
| title_sort | mining student information system records to predict students’ academic performance |
| topic | LB2300 Higher Education QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/24977/1/Mining%20student%20information%20system%20records%20to%20predict%20students%E2%80%99.pdf http://umpir.ump.edu.my/id/eprint/24977/ https://doi.org/10.1007/978-3-030-14118-9_23 |
| url_provider | http://umpir.ump.edu.my/ |