Using Data Mining in Educational Administration: A Case Study on Improving School Attendance Chiclana Parrilla, Francisco Moodley, Raymond Educational data mining Association rule mining Improving school attendance Persistent absenteeism The authors would like to thank the leadership and staff of Willen Primary School for permitting us to use their data and for their efforts in supporting this study, in particular, Ms Emma Warner (attendance officer), Ms Carrie Matthews (headteacher), and Ms Sarah Orr (deputy headteacher). 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. 2020-06-22T12:01:32Z 2020-06-22T12:01:32Z 2020-04 journal article Moodley, R., Chiclana, F., Carter, J., & Caraffini, F. (2020). Using Data Mining in Educational Administration: A Case Study on Improving School Attendance. Applied Sciences, 10(9), 3116. [doi:10.3390/app10093116] http://hdl.handle.net/10481/62609 10.3390/app10093116 eng http://creativecommons.org/licenses/by/3.0/es/ open access Atribución 3.0 España MDPI