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

PDF

Identifier

DP0028974

Language

English

Release Date

12-15-2024

Access Status

Dissertation

Campus Location

Orlando (Main) Campus

Accessibility Status

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