ORCID
Predicting Post-School Outcomes of Transition Aged Students With High Incidence Disabilities
Keywords
Predictive Modeling, Transition Planning, High-Incidence Disabilities, Parental Involvement, Machine Learning, Post-School Outcomes
Abstract
This dissertation explores predictive modeling to forecast post-school outcomes for transition-aged students with high-incidence disabilities, such as Specific Learning Disabilities (SLD) and Other Health Impairments (OHI). Transitioning from secondary to post-secondary environments is critical for students with disabilities, significantly impacting their independence, self-confidence, and employability (Morningstar et al., 2017; Carter et al., 2021). Through a mixed-methods approach, this study integrates Office of Special Education Programs (OSEP) indicators to determine which factors most effectively predict post-secondary enrollment and graduation. Key variables such as parental involvement, socio-economic status (SES), and self-determination skills are analyzed using logistic regression and machine learning techniques, including neural networks, decision trees, and Naive Bayes models (Raschka & Mirjalili, 2019; Chan et al., 2023).
The findings indicate that parental involvement, SES, and self-determination are significant predictors of post-school success (Sirin, 2005; Anders et al., 2020). Additionally, machine learning models outperform traditional methods in terms of accuracy and precision, providing nuanced insights into student trajectories (Raschka & Mirjalili, 2019). The research underscores the importance of early identification and customized interventions tailored to the specific needs of students with disabilities, emphasizing the need for stronger family-school partnerships and targeted policies to address socio-economic disparities (Kearns et al., 2020; Schmidt et al., 2020). These insights offer educators and policymakers evidence-based strategies to improve educational and vocational outcomes for students with disabilities, facilitating smoother transitions into adulthood.
Completion Date
2024
Semester
Fall
Committee Chair
Matthew Marino
Degree
Doctor of Philosophy (Ph.D.)
College
College of Community Innovation and Education
Department
Education
Degree Program
Exceptional Student Education
Format
Identifier
DP0028974
Language
English
Release Date
12-15-2024
Access Status
Dissertation
Campus Location
Orlando (Main) Campus
STARS Citation
Brewer, Jacob, "Predicting Post-School Outcomes of Transition Aged Students With High Incidence Disabilities" (2024). Graduate Thesis and Dissertation post-2024. 13.
https://stars.library.ucf.edu/etd2024/13
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