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Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
,
Francisco Chiclana ,
Jenny Carter,
Fabio Caraffini
Applied Sciences, Volume: 10, Issue: 9, Start page: 3116
Swansea University Author:
Fabio Caraffini
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Copyright: 2020 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
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DOI (Published version): 10.3390/app10093116
Abstract
Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the...
| Published in: | Applied Sciences |
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| ISSN: | 2076-3417 |
| Published: |
MDPI AG
2020
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60957 |
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| Abstract: |
Pupil absenteeism remains a significant problem for schools across the globe with negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96%. A novel approach is proposed to help schools improve attendance that leverages the market target model, which is built on association rule mining and probability theory, to target sessions that are most impactful to overall poor attendance. Tests conducted at Willen Primary School, in Milton Keynes, UK, showed that significant improvements can be made to overall attendance, attendance in the target session, and persistent (chronic) absenteeism, through the use of this approach. The paper concludes by discussing school leadership, research implications, and highlights future work which includes the development of a software program that can be rolled-out to other schools. |
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| Keywords: |
educational data mining; association rule mining; improving school attendance; persistent absenteeism |
| College: |
Faculty of Science and Engineering |
| Funders: |
This research received no external funding. |
| Issue: |
9 |
| Start Page: |
3116 |