Teacher performance is a critical determinant of educational quality but remains influenced by the quality of academic supervision, work motivation, and digital literacy. A quantitative causal-associative design was applied to a sample of 134 teachers determined by the Isaac–Michael table. Data were collected via a four-point Likert questionnaire and analyzed using multiple linear regression after verifying normality, linearity, multicollinearity, and heteroscedasticity assumptions. The F-test confirmed a very significant simultaneous effect (F = 819.493; p < 0.001). The coefficient of determination (R² = 0.950) indicated that 95% of the variance in teacher performance was explained by the model, with the remaining 5% due to other factors. Work motivation and digital literacy are the primary determinants of teacher performance; academic supervision should be redesigned to be more reflective, collaborative, and coaching-based. These findings enrich educational research and offer a data-driven basis for local governments in crafting sustainable teacher capacity-building initiatives.
Damanik et al. (Thu,) studied this question.