Predicting Student Outcomes in Special Education: The Role of Teacher Well-Being Dimensions through Multiple Regression Analysis

Abstract

Teacher well-being has been identified as a critical yet empirically underexamined determinant of instructional quality in special education, particularly within Asian contexts. Grounded in Allardt's Sociological Welfare Theory (1989) and Bloom's Taxonomy (Bloom et al., 1956), this quantitative survey study examined the level of teacher well-being, its relationship with, and predictive influence on the outcomes of students with special educational needs (SEN) among 437 primary school special education teachers in Peninsular Malaysia, selected through multistage sampling. Descriptive findings indicated that overall teacher well-being was at a very high level (M = 4.23, SD = 0.44), with self-achievement (M = 4.27) and health (M = 4.25) rated very high, while school environmental conditions were rated high (M = 4.18). Pearson correlation analysis revealed a strong, positive, and statistically significant relationship between teacher well-being and SEN student outcomes (r = .692, p < .01), reflecting a large effect size. Multiple regression analysis further demonstrated that all three dimensions of teacher well-being significantly predicted SEN student outcomes, with school environmental conditions emerging as the strongest predictor (β = .341, p < .001), followed by self-achievement (β = .257, p < .001) and health (β = .196, p < .001). The model collectively accounted for 48.1% of the variance in SEN student outcomes (R² = .481, adjusted R² = .477), indicating substantial predictive power. These findings suggest that structural and environmental factors exert greater influence on SEN student outcomes than individual-level well-being attributes, underscoring the need for targeted investment in school infrastructure, teacher professional development, and institutional well-being support mechanisms within Malaysian special education settings.