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Using Data Mining in Educational Administration: A Case Study on Improving School Attendance

Raymond Moodley Orcid Logo, Francisco Chiclana Orcid Logo, Jenny Carter, Fabio Caraffini Orcid Logo

Applied Sciences, Volume: 10, Issue: 9, Start page: 3116

Swansea University Author: Fabio Caraffini Orcid Logo

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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...

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Published in: Applied Sciences
ISSN: 2076-3417
Published: MDPI AG 2020
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URI: https://cronfa.swan.ac.uk/Record/cronfa60957
first_indexed 2022-09-21T13:42:48Z
last_indexed 2023-01-13T19:21:28Z
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spelling 2022-09-21T14:44:24.0757918 v2 60957 2022-08-28 Using Data Mining in Educational Administration: A Case Study on Improving School Attendance d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 MACS 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. Journal Article Applied Sciences 10 9 3116 MDPI AG 2076-3417 educational data mining; association rule mining; improving school attendance; persistent absenteeism 29 4 2020 2020-04-29 10.3390/app10093116 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This research received no external funding. 2022-09-21T14:44:24.0757918 2022-08-28T20:46:51.0724191 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Raymond Moodley 0000-0003-4471-2272 1 Francisco Chiclana 0000-0002-3952-4210 2 Jenny Carter 3 Fabio Caraffini 0000-0001-9199-7368 4 60957__25184__f06c5ee985d247998eb13623f075a4a8.pdf 60957_VoR.pdf 2022-09-21T14:43:11.1154071 Output 404085 application/pdf Version of Record true 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 true eng http://creativecommons.org/licenses/by/4.0/
title Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
spellingShingle Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
Fabio Caraffini
title_short Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
title_full Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
title_fullStr Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
title_full_unstemmed Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
title_sort Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Raymond Moodley
Francisco Chiclana
Jenny Carter
Fabio Caraffini
format Journal article
container_title Applied Sciences
container_volume 10
container_issue 9
container_start_page 3116
publishDate 2020
institution Swansea University
issn 2076-3417
doi_str_mv 10.3390/app10093116
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description 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.
published_date 2020-04-29T08:09:33Z
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