Demographic and environmental predictors of self-determination in Turkish students with and without disabilities: A classical and machine learning-based analysis.

BACKGROUND: The substantial variability in educational opportunities across individual and environmental conditions in Türkiye provides a meaningful context for examining associations between self-determination and contextual factors. However, the absence of culturally adapted student-report measures continues to constrain empirical analyses of these relationships.

AIM: This study (1) adapted and validated the Turkish version of the AIR Self-Determination Scale-Student Form and (2) identified contextual predictors of self-determination using both classical statistical analyses and machine learning modeling. Guided by Causal Agency Theory, the study explored the interaction between students' self-determination capacities and perceived opportunities across diverse educational settings in Türkiye.

RESULTS: Psychometric analyses (n = 342) confirmed the four-factor capacity-opportunity structure and showed strong internal consistency. In a second sample (n = 501), self-determination scores varied by disability status, age, school type, residential area, and rehabilitation support. Capacity and opportunity were moderately correlated. ML models explained substantial variance in total scores and identified school type and disability status as the strongest predictors.

CONCLUSION: Findings suggest that self-determination is linked to both individual characteristics and contextual opportunity structures. Enhancing autonomy-supportive practices and using data-informed approaches to identify students with limited opportunities may support stronger self-determination outcomes in Türkiye.