The Influence of Deep Learning Strategies and Classroom Climate on Vocational Teacher Performance in Indonesian Public Vocational High Schools
DOI:
https://doi.org/10.52690/jswse.v7i2.1486Keywords:
Classroom Climate, Deep Learning Strategies, Indonesian Vocational Education, Teacher Performance, Vocational PedagogyAbstract
Vocational teacher performance remains a critical determinant of educational quality, yet factors influencing this performance in regional Indonesian contexts remain underexplored. This study examines the influence of deep learning strategies and classroom climate on vocational teacher performance in public vocational high schools in North Bengkulu Regency, Indonesia. Using an ex post facto causal correlational design, data were collected from 122 vocational teachers through validated Likert-scale questionnaires. Simple and multiple linear regression analyses revealed that deep learning strategies significantly predict teacher performance (β = 0.412, p < 0.05), as does classroom climate (β = 0.587, p < 0.05), with classroom climate exhibiting a stronger influence. The combined model explained 68.3% of variance in teacher performance (R² = 0.683, F = 128.45, p < 0.001). Theoretical interpretation through Job Demands-Resources (JD-R) and Self-Determination Theory (SDT) frameworks reveals that organizational resources (climate) provide foundational support enabling pedagogical innovation, while supportive climates fulfill teachers' basic psychological needs for competence, autonomy, and relatedness. This study contributes novel insights into vocational education in non-metropolitan Indonesian settings, highlighting that teacher performance emerges from synergistic interaction between pedagogical approaches and environmental conditions. The findings carry critical implications for educational policy in developing contexts: policymakers must prioritize establishing supportive organizational climates through professional learning communities, adequate resource allocation, and administrative support systems before mandating pedagogical reforms, as deep learning implementation without corresponding climate investment creates unsustainable demand-resource imbalances that undermine rather than enhance teacher effectiveness. This resource-first, pedagogy-second sequencing represents a fundamental departure from conventional deficit models and offers a replicable framework for improving vocational education quality in resource-constrained regions globally.
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