Gradient Boosting Public Data Modeling for the Policy Planning in Education

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dc.contributor.advisorTesic, Jelena
dc.contributor.authorYu, June
dc.contributor.committeeMemberWang, Lu
dc.contributor.committeeMemberFeng, Li
dc.date.accessioned2022-12-08T20:18:13Z
dc.date.available2022-12-08T20:18:13Z
dc.date.issued2022-12
dc.description.abstractTeacher retention rate and student learning gain rates in the U.S. public school systems plummeted during COVID-19 pandemic, erasing years of improvements. In this body of research, we collect, integrate, and analyze all available public data in the data science pipeline to see if public data can inform and impact the factors of teacher attrition and learning loss. This is the first known study of the public data to address the post-COVID educational policy crisis from a data science perspective. To this end, we have developed an end-to-end large-scale educational data modeling pipeline that (i) integrates, cleans, and analyzes educational data; (ii) implements automated attribute importance analysis to draw meaningful conclusions; and (iii) develops a suite of interpretable teacher attrition and learning loss prediction models utilizing all data points and attributes. We demonstrate a novel data-driven approach to discover insights from a large collection of heterogeneous public data sources and to offer an actionable understanding to policymakers about the (1) recruitment and retention of public teachers, and (2) identifying learning loss tendencies and prevention of them in public schools.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent73 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationYu, J. (2022). Gradient boosting public data modeling for the policy planning in education (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/16373
dc.language.isoen
dc.subjecteducational data science
dc.subjectteacher attrition
dc.subjectteacher retention
dc.subjectlearning loss
dc.subjectpredictive modeling, education policy
dc.subjecttabular data
dc.subjectmachine learning
dc.subjectgradient boosting
dc.subjecteducation policy
dc.titleGradient Boosting Public Data Modeling for the Policy Planning in Education
dc.typeThesis
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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