Data-driven analytics to explore associations between risk and protective factors and school absenteeism for secondary school students
2021-07
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Data-driven analytics to explore associations between risk and protective factors and school absenteeism for secondary school students
Authors
Published Date
2021-07
Publisher
Type
Thesis or Dissertation
Abstract
Study PurposeChronic absenteeism (CA) is an administrative term defining extreme failure for students to be present at school. CA is recognized as a national problem in the U.S. that has devastating long-term impacts on students. However, in consideration of what counts as students missing school, the partial-day absence (PDA) is inconsistently used across the U.S. as opposed to the full-day absence (FDA). This is because the impact of PDA on student outcomes is less studied due to diverse policies at the local school district level. Applying causal discovery analysis techniques to student-level data, this study analyzed the interconnectivity of partial-day absence and full-day absence by comparing risk and protective factors operationalized by specific student-reported factors were included in the analysis based on Bronfenbrenner’s bioecological model of development.
Methods
Using machine learning techniques (i.e. feature selection, prediction model performance comparison) on de-identified student-level data (n = 121,005) from the Minnesota Student Survey 2016, factors associated with school absences were identified as the Aim 1. For Aim 2, which was conducting a mixed-methods approach, a focus-group interview with licensed school nurses (LSNs) in Minnesota helped to identify factors associated with CA and how it’s different between PDA and FDA in a qualitative perspective. Then a mixed-methods approach utilizing a casual discovery method was conducted using the Minnesota Student Survey 2019 (n = 125,375). In the mixed-methods approach, identified factors and knowledge gained from both the quantitative (feature selection and prediction model performance comparison) and qualitative (LSNs focus-group interview) approaches were used separately and also combined to compare and validate the results during the causal discovery analysis process.
Results
For the Aim 1, a total of 18 risk and protective factors (out of 113) associated with school absences were identified which were within either micro- or mesosystem in the bioecological multisystem. With the results of Aim 1 and LSN focus-group interview, causal discovery analyses were conducted. Findings indicated a) PDA directly affecting FDA, b) PDA shown to be the main linkage between FDA and other school absences surrounding factors (e.g. school engagement, student-teacher relationships), and c) an implication of PDA covering school absence related factors within micro-, meso-, and macrosystem which is wider than that of FDA (i.e. only directly affected by factors within micro- and mesosystem).
Implications
Results suggest PDA’s fundamental differences with FDA which calls for recognition of PDA in the field of school absences. This dissertation study also revealed the current impact the LSNs have on students who are missing schools (i.e. assessing the student-in-risk for CA, providing breakfast or space for support) from the focus-group interview with current limitations they have such as low student to school nurse ratio which was also reflected in the data used in quantitative approaches. From these results, future researchers would benefit from differentiating school absences into PDA and FDA as it enables those studies to point out which aspect of school absences they are focusing on. Also, attention to validating what’s identified in this study is needed, i.e., Utilizing data from different time periods to replicate the results as the study only served its purpose as an exploratory study of PDA. Locating the data with a) a sufficient amount of LSN features, b) a balanced ratio of factors throughout the hierarchical multisystem (i.e. factors from micro-, meso-, exo-, macrosystem), and c) a definition of CA used which are unexcused and excused absences combined will help to better understand the interconnection of school absences surrounding factors and LSNs.
Keywords
Description
University of Minnesota Ph.D. dissertation. July 2021. Major: Nursing. Advisor: Connie Delaney. 1 computer file (PDF); xv, 229 pages.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Lee, Gunwoo. (2021). Data-driven analytics to explore associations between risk and protective factors and school absenteeism for secondary school students. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/224932.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.