Individual, Family, and Community Education ETDs
Publication Date
Summer 7-28-2026
Abstract
High school dropout remains a persistent educational challenge with significant socio-economic consequences. This dissertation investigates dropout prediction through three interrelated studies utilizing machine learning (ML) and national longitudinal data. A systematic review study identified a surge in ML applications since 2015 and a critical need to move beyond student-level metrics to include broader contextual factors. Comparative analysis of ML methods demonstrated that while Linear Discriminant Analysis (LDA) exhibits the best performance at standard thresholds, lowering the classification threshold enhances sensitivity across all models, and informed the development of a Dropout Risk Framework encompassing academic, belief, behavioral, and contextual domains. Longitudinal comparison between the 2000s (ELS:2002) and 2010s (HSLS:09) cohorts found that while GPA and attendance remain foundational, socioemotional factors have gained predictive prominence. Ultimately, this dissertation affirms that calibrated ML models provide actionable insights for early warning systems.
Keywords
Machine Learning, High School, Dropout Prediction, At-risk students, Educational Data Mining, Socioemotional Factors
Document Type
Dissertation
Language
English
Degree Name
Educational Psychology
Level of Degree
Doctoral
Department Name
Individual, Family, and Community Education
First Committee Member (Chair)
Yu-Yu Hsiao
Second Committee Member
Carolyn J. Hushman
Third Committee Member
Yen Pham
Fourth Committee Member
Shengjie Lin
Recommended Citation
Wang, Qing. "Data Driven Insights into High School Dropout Prediction: Machine Learning Applications." (2026). https://digitalrepository.unm.edu/educ_ifce_etds/174
Included in
Educational Assessment, Evaluation, and Research Commons, Educational Psychology Commons, Secondary Education Commons