A Systematic Review on Educational Data Mining
Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7820050/ |
| _version_ | 1828891809794752512 |
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| author | Ashish Dutt Maizatul Akmar Ismail Tutut Herawan |
| author_facet | Ashish Dutt Maizatul Akmar Ismail Tutut Herawan |
| author_sort | Ashish Dutt |
| collection | DOAJ |
| description | Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified. |
| first_indexed | 2024-12-13T13:24:50Z |
| format | Article |
| id | doaj.art-2903771623034a379c15e1f30094ec5a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:24:50Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2903771623034a379c15e1f30094ec5a2022-12-21T23:44:19ZengIEEEIEEE Access2169-35362017-01-015159911600510.1109/ACCESS.2017.26542477820050A Systematic Review on Educational Data MiningAshish Dutt0Maizatul Akmar Ismail1Tutut Herawan2https://orcid.org/0000-0001-9262-9137Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MalaysiaDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, MalaysiaPresently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.https://ieeexplore.ieee.org/document/7820050/Data miningclustering methodseducational technologysystematic review |
| spellingShingle | Ashish Dutt Maizatul Akmar Ismail Tutut Herawan A Systematic Review on Educational Data Mining IEEE Access Data mining clustering methods educational technology systematic review |
| title | A Systematic Review on Educational Data Mining |
| title_full | A Systematic Review on Educational Data Mining |
| title_fullStr | A Systematic Review on Educational Data Mining |
| title_full_unstemmed | A Systematic Review on Educational Data Mining |
| title_short | A Systematic Review on Educational Data Mining |
| title_sort | systematic review on educational data mining |
| topic | Data mining clustering methods educational technology systematic review |
| url | https://ieeexplore.ieee.org/document/7820050/ |
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