Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students

Knoo Lee; Barbara J. McMorris; Chih Lin Chi; Wendy S. Looman; Matthew K. Burns; Connie W. Delaney

doi:10.1016/j.jsp.2023.03.002

Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students

Knoo Lee, Barbara J. McMorris, Chih Lin Chi, Wendy S. Looman, Matthew K. Burns, Connie W. Delaney

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in “The Kids and Teens at School” (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences. A total of 18 risk and protective variables were identified; all 18 variables were characteristics of the microsystem or mesosystem, emphasizing school absences' proximity to variables within inner ecological systems rather than the exosystem or macrosystem. Implications for future studies and health infrastructure are discussed.

Original language	English (US)
Pages (from-to)	148-180
Number of pages	33
Journal	Journal of school psychology
Volume	98
DOIs	https://doi.org/10.1016/j.jsp.2023.03.002
State	Published - Jun 2023

Bibliographical note

Funding Information:
Knoo Lee: Conceptualization, Data curation, Resources, Software, Formal analysis, Validation, Visualization, Writing - original draft, review & editing. Barbara J. McMorris: Conceptualization, Data curation, Validation, Writing - review & editing. Chih-Lin Chi: Data curation, Validation, Writing - review & editing. Wendy S. Looman: Conceptualization, Methodology, Writing - review & editing. Matthew K. Burns: Validation, Writing - review & editing. Connie W. Delaney: Conceptualization, Supervision, Project administration, Writing - review & editing.

Publisher Copyright:
© 2023 Society for the Study of School Psychology

Keywords

Chronic absenteeism
Data-informed theory-driven study
Ecological systems theory
Machine learning, predictive modeling
School absences

PubMed: MeSH publication types

Journal Article

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10.1016/j.jsp.2023.03.002

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title = "Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students",

abstract = "Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in “The Kids and Teens at School” (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences. A total of 18 risk and protective variables were identified; all 18 variables were characteristics of the microsystem or mesosystem, emphasizing school absences' proximity to variables within inner ecological systems rather than the exosystem or macrosystem. Implications for future studies and health infrastructure are discussed.",

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author = "Knoo Lee and McMorris, {Barbara J.} and Chi, {Chih Lin} and Looman, {Wendy S.} and Burns, {Matthew K.} and Delaney, {Connie W.}",

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AU - Delaney, Connie W.

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N2 - Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in “The Kids and Teens at School” (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences. A total of 18 risk and protective variables were identified; all 18 variables were characteristics of the microsystem or mesosystem, emphasizing school absences' proximity to variables within inner ecological systems rather than the exosystem or macrosystem. Implications for future studies and health infrastructure are discussed.

AB - Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in “The Kids and Teens at School” (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences. A total of 18 risk and protective variables were identified; all 18 variables were characteristics of the microsystem or mesosystem, emphasizing school absences' proximity to variables within inner ecological systems rather than the exosystem or macrosystem. Implications for future studies and health infrastructure are discussed.

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Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students

Abstract

Bibliographical note

Keywords

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