Quality Evaluation as Strategic Early Warning System for Decision-Making in Vocational Polytechnic

Abstract

Quality evaluation is a crucial instrument for ensuring that vocational higher education institutions achieve their strategic objectives effectively. This study examines how evaluation data are utilized to support strategic decision-making in a vocational higher education institution. The evaluation process integrates data from multiple sources, including stakeholder satisfaction surveys, monthly monitoring and evaluation reports, and internal quality audits. A mixed methods approach was employed by combining document analysis, survey data, and focused interviews with key institutional stakeholders over a one-year period.

The findings indicate that quality evaluation functions not only as a tool for identifying institutional strengths and weaknesses, but also as an early warning system for strategic risks. The evaluation results reveal the need to strengthen academic governance through the implementation of structured student admission procedures, an integrated academic calendar, and updated selection instruments. In addition, the optimization of digital services and institutional websites is essential to support a safe and inclusive campus environment. From an academic perspective, strategic priorities include curriculum alignment with industry needs, the enhancement of certified practitioner lecturer selection, and strengthened monitoring of internships, project-based learning, and research downstreaming. Furthermore, the library and research management units are encouraged to improve organizational structures, service systems, and research roadmaps aligned with the Sustainable Development Goals and artificial intelligence development.

This study demonstrates that data-driven strategic decision-making based on quality evaluation can enhance institutional competitiveness and sustainability in vocational higher education. The findings contribute to the development of an evaluation-based strategic management model and provide practical insights for institutions seeking to strengthen continuous quality improvement.