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

Date
2020-04-29
Authors
Moodley, Raymond
Chiclana, Francisco
Carter, Jenny
Caraffini, Fabio
Journal Title
Journal ISSN
ISSN
Volume Title
Publisher
MDPI
Peer reviewed
Yes
Abstract
Pupil absenteeism remains a significant problem for schools across the globe with its 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, show 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.
Description
open access article
Keywords
Educational Data Mining, Association Rule Mining, Improving School Attendance, Persistent Absenteeism
Citation
Moodley, R., Chiclana, F., Caraffini, J.C.F. (2020) Using Data Mining in Educational Administration: A Case Study on Improving School Attendance. Applied Science,10(9), pp.3116.
Research Institute
Institute of Artificial Intelligence (IAI)