Translating sophisticated data analytic strategies regarding school attendance and absenteeism into targeted educational policy

Abstract

School attendance and absenteeism are critical targets of educational policies and practices that often depend heavily on aggregated attendance/absenteeism data. School attendance/absenteeism data in aggregated form, in addition to having suspect quality and utility, minimizes individual student variation, distorts detailed and multilevel modeling, and obscures underlying causes and disparities of absenteeism. Recent advances in data analytics/mining and modeling may assist researchers and other stakeholders by evaluating large-scale data sets in more targeted ways to identify key root causes and patterns of school absenteeism in a particular community, school, or group of students. This would allow for more accurate educational policies tailored to unique local conditions and student/family circumstances. This article provides a summary of recent algorithm- and model-based efforts in this regard. Algorithm-based efforts include classification and regression tree analysis, ensemble analysis, support vector machines, receiver operating characteristic analysis, and random forests. Model-based efforts include multilevel modeling, structural equation modeling, latent class analysis, and meta-analytic modeling. We then illustrate how these efforts can enhance a full and nuanced understanding of the root, interconnected causes of absenteeism, improve early warning systems, and assist multi-tiered systems of support interventions for absenteeism.

From Attendance Data to Student Support: International Practices for Recording, Reporting, and Using Data on School Attendance and Absence

“…Moreover, embracing technological advancements offers exciting prospects for efficiently collecting DSAA (Ishak & Bibi, n.d.), and thus for the analysis and use of DSAA. By implementing technological solutions for attendance tracking, early warning systems, and data analysis, we can enhance decision-making and support evidence-based practices within the field of school attendance (Kearney & Childs, 2023). While these innovative approaches open new avenues for understanding attendance patterns and implementing effective strategies to improve student outcomes, challenges such as data security need to be addressed (Ishak & Bibi, n.d.).…”