# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 1 Introduction  
Pupil attendance remains a key focus for schools, local authorities, and national governments across the world as a result of its strong, positive correlation with pupil attainment, pupil well-being, and improved economic outcomes for pupils later in life [1; 2; 3]. In the UK, the Department for Education (DfE) has strict policies on school attendance with legal obligations for both parents, which also includes guardians in this study, and schools [4]. Parents are legally obliged to send their children to school and ensure regular attendance, while schools have a legal duty to take the necessary steps and have policies in place to effectively manage pupil attendance [4]. In this regard, there is a significant requirement from schools to be proactive on attendance management as they must accurately record attendance, proactively follow-up with parents on all absences, and put initiatives in place to manage and encourage good attendance [4].  
As further clarified in Section 2.2.1, the underlying reasons as to why pupils are absent from school have been well-studied and generally fall into one, or more, of three categories: (1) Unable to attend school due to other obligations (e.g., illness, care duties, family instability), (2) avoiding school due to fear, embarrassment, boredom (e.g., being bullied), and (3) pupil/family do not place value in schooling and/or have other activities that they would rather do, e.g., taking a vacation, or high levels of illiteracy within the family [1; 5]. To this end, strategies for managing absenteeism(predominantly qualitative) have also been well-studied with models, frameworks, and initiatives for improving school attendance being proposed and evaluated [1; 2; 3; 5]. The quantitative approaches involving school attendance has primarily been seen as a task within the Educational Data Mining (EDM) branch of research, where the key objective is to improve pupil performance through the use of data mining and artificial intelligence (AI) techniques [6]. Indeed, there have been several models proposed to predict pupil outcomes, however, attendance has typically been used as an input variable for these models as opposed to being a key focus area [6; 7; 8; 9; 10].  
The case for increased use of data analytics and AI to improve attendance has been well-made, however very little use-cases and readily available analytical models exist that can easily be adopted by school practitioners to improve school attendance [1; 2]. Furthermore, most school practitioners are new to data analytics and have no previous data science background. Despite the current availability of training and certification courses, it is often challenging for practitioners to develop their models and algorithms to conduct a deep analysis of data [11]. It is against this backdrop that this study aims to provide school practitioners with a simple, yet effective model to improve school attendance by identifying and acting on attendance patterns that are not obvious to extrapolate without data analytic skills. The proposed model was applied to a live setting using Willen Primary School (WPS), a local authority-maintained primary school in Milton Keynes, UK, as a case study. In principle, the approach, findings, and recommendations from this study can be leveraged by other schools wanting to improve pupil attendance. In this regard, intervention programs may have to be adapted to cater for the school's specific circumstances.  
The remainder of the paper is divided as follows: An overview of the relevant literature is provided in Section 2 followed by a detailed description of the problem statement and development of the underlying analytical model in Section 3. The research methodology is outlined in Section 4, with a presentation of the results and discussion in Section 5. Finally, conclusions and future work are detailed in Section 6.