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.
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Conditions assessed at the aggregate level can mask within-district disparities, such that outcomes appearing adequate overall may be considerably worse for specific groups.
“Kearney and Childs (2023) note that conditions assessed at the aggregate level can mask within-district disparities, such that outcomes appearing adequate overall may be considerably worse for specific groups.”
From Policy Silence as Governance: AI Decision-Making, Responsibility, and Risk in K–12 Education (2026) · cites this paper as Kearney and Childs (2023)
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Sideridis, G. D., Tsaousis, I., & Al-Harbi, K. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.624221 · Cited by 7 · see citing works
Racial Disparities in the Education System
Teasley, M. L., & Homer, B. (2020) · Encyclopedia of Social Work · doi:10.1093/acrefore/9780199975839.013.1290 · Cited by 6 · see citing works
What Pittsburgh Is Doing to Ensure Every Child Has a Fighting Chance to Succeed
Childs, J. (2017) · National Civic Review · doi:10.1002/ncr.21315 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
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Sorted by citations
Kearney, C. A., Dupont, R., Fensken, M., & Gonzálvez, C. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1253595 · Cited by 71 · see citing works
Self-citationA systematic review of intervention programs for school attendance problems.
Pérez-Marco, M., Gonzálvez, C., Fuster, A., & Vicent, M. (2025) · Children and Youth Services Review · doi:10.1016/j.childyouth.2024.108091 · Cited by 32 · see citing works
Kearney, C. A., Fensken, M., & Dupont, R. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1553528 · Cited by 11 · see citing works
Self-citationHeyne, D., Keppens, G., & Dvořák, D. (2024) · Orbis Scholae · doi:10.14712/23363177.2023.16 · Cited by 10 · see citing works
Kearney, C. A., Fensken, M., & Dupont, R. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1272297 · Cited by 8 · see citing works
Self-citationFraming chronic absenteeism and emotionally-based school absenteeism as public health problems
Kearney, C. A. (2025) · Frontiers in Child and Adolescent Psychiatry · doi:10.3389/frcha.2025.1662093 · Cited by 5 · see citing works
Self-citationCollin, S. M., & Hennetier, C. (2024) · Médiations et médiatisations · doi:10.52358/mm.vi18.412 · Cited by 3 · see citing works
Çimen, F., Seçer, İ., & Ay, E. (2025) · School Mental Health · doi:10.1007/s12310-025-09824-4 · Cited by 2 · see citing works
School attendance and absenteeism: Towards next‐generation common measurement
Kearney, C. A., Benoit, L., Gonzálvez, C., Heyne, D., Melvin, G., O’Toole, C., Genitty, C. G., & Keppens, G. (2026) · Revista de educación · doi:10.1002/rev3.70146 · Cited by 2 · see citing works
Self-citationPolicy Silence as Governance: AI Decision-Making, Responsibility, and Risk in K–12 Education
Miles, A. J., & Arar, K. (2026) · Improving Schools · doi:10.1177/13654802261470692
Students Motivation Strategies and Attitudes in Learning Social Sciences: Basis for an Action Plan
Biboso, J., & Cruz, C. J. B. D. (2025) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.380708
Ulum, Ö. G. (2025) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2025.100513
Djamel, B. (2025) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.3(47).2025.4381
Owiredua, C., Flink, I., Evans, B., & Boersma, K. (2026) · PAIN Reports · doi:10.1097/pr9.0000000000001416 · Cited by 1 · see citing works
Advancing socially just and anti-racist approaches for school attendance problems
Kearney, C. A., Ellis, K., & Arcaina, V. J. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.102779
Self-citationA Machine Learning Approach to Classifying School Absenteeism Patterns in Youth with ADHD
Goh, P. K., Rogers, M. A., Wong, A. W., Mellor, G. S., Whitley, J., Smith, J., McBrearty, N., Tatartcheff-Quesnel, N., & DuPaul, G. (2026) · School Mental Health · doi:10.1007/s12310-026-09893-z
Munro, C., & White, M. O. (2026) · Child and Adolescent Social Work Journal · doi:10.1007/s10560-026-01133-2
- Authors
- Kearney, C. A., & Childs, J.
- Affiliations
- University of Nevada, Las Vegas (US)
- The University of Texas at Austin (US)
- Corresponding author
- Kearney, Christopher A.
- Venue
- Improving Schools
- Publisher
- SAGE Publications
- Published
- 2023-03
- Volume
- 26
- Issue
- 1
- Pages
- 5-22
- ISSN
- 1365-4802 (ISSN-L)
- 1475-7583
- OpenAlex
- W4378229443
- Cited by
- 17
- References
- 113
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 4 · 2023: 1
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Improving Schools (publishedVersion)
- Topics
- Social Sciences › Education › Youth Substance Use and School Attendance
- Social Sciences › Decision Sciences › Information Systems and Management › Educational Assessment and Improvement
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- school absenteeism, school attendance, education policy, data analytics, data mining, classification and regression trees, ensemble methods, support vector machine, random forest, multilevel modeling, latent class analysis, structural equation modeling, early warning systems, multi-tiered systems of support
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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