자료유형  

 학위논문

Control Number  

0017162410

International Standard Book Number  

9798383563168

Dewey Decimal Classification Number  

379.1

Main Entry-Personal Name  

Loan, Christopher M.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Oregon., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

218 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

General Note  

Advisor: Zvoch, Keith.

Dissertation Note  

Thesis (Ph.D.)--University of Oregon, 2024.

Summary, Etc.  

요약Simulations were conducted to establish best practice in hyperparameter optimization and accounting for clustering in Generalized Linear Mixed-Effects Model Trees (GLMM trees). Using data-driven best practices, the relationship between a 9th Grade On-Track to Graduate (9G-OTG) indicator and observed high school graduation within four years was explored. Data originated from two cohorts of the Oregon State Longitudinal Data System (SLDS) and were joined with external datasets. Restricted to complete cases, the data were comprised of more than 58,000 observations, each with more than 1500 variables measured at student, school, district, and zip code levels. GLMM trees explored heterogeneity in a cross-classified multilevel logistic regression which regressed observed graduation on 9G-OTG, accounting for variance in school- and zip-code-level random intercepts. Subgroups were identified for whom the probability of graduating among on- and-off track students were systematically heterogeneous, relative to the supraordinate group. Results suggest that for most students, 9G-OTG is a potent early warning indicator of graduation, but systematic variation in the indicator's effectiveness was found along all levels except district. Subgroups were defined by combinations of alternative schools, absences, transferring schools, being enrolled in more than one instructional program, neighborhood unemployment, and sex. Implications and recommendations to measurement, practice, and evaluation are discussed.

Subject Added Entry-Topical Term  

Educational evaluation.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Education policy.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Information technology.

Index Term-Uncontrolled  

Graduation

Index Term-Uncontrolled  

Hyperparameter optimization

Index Term-Uncontrolled  

Model-based recursive partitioning

Index Term-Uncontrolled  

Multilevel modeling

Index Term-Uncontrolled  

State Longitudinal Data Systems

Added Entry-Corporate Name  

University of Oregon Department of Education Studies

Host Item Entry  

Dissertations Abstracts International. 86-01B.

Electronic Location and Access  

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Control Number  

joongbu:657196