<![CDATA[Teacher performance management is a critical component of efforts to improve learning quality, yet its effectiveness depends on how systematically performance is monitored, evaluated, and developed. This study examines the role of academic supervision and sustained feedback in strengthening teacher performance management and contributing to improved learning quality. A qualitative descriptive approach was employed involving 12 teachers selected purposively based on their direct involvement in teaching and experience with academic supervision and performance evaluation. Data were collected through semi-structured interviews, classroom observations, and document analysis, and were analyzed through data condensation, data display, and conclusion drawing and verification. The findings reveal three interconnected patterns. First, structured academic supervision strengthens teacher performance management by providing evidence-based information regarding instructional strengths, weaknesses, and professional development needs. Second, sustained feedback functions as a continuous mechanism that connects performance evaluation with teacher reflection, improvement actions, and follow-up monitoring. Rather than functioning as a one-time evaluative response, feedback becomes a recurring process that supports teachers in refining their instructional practices. Third, the integration of academic supervision and sustained feedback contributes to improvements in teaching effectiveness, classroom management, and the organization of learning activities, thereby creating more supportive conditions for learning quality. The study argues that the contribution of supervision to learning quality is not immediate or linear but operates through a continuous teacher performance development process. The integration of supervision, constructive feedback, reflection, and follow-up monitoring therefore provides a more sustainable framework for strengthening teacher performance management and promoting continuous improvement in classroom learning]]>
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