The project-vector management method application in higher education quality assessment - Mukhatayev, Aidos; Omirbayev, Serik; Kuchanskyi, Oleksandr; Biloshchytskyi, Andrii; Andrashko, Yurii; Biloshchytska, Svitlana; Kazambayev, Ilyas; Ispussinov, Aidar (Frontiers in Education, 2025)
Higher education system of Kazakhstan remains in transition, where preventive state regulation, fragmented evaluation procedures, and limited institutional autonomy still shape quality assurance practices. Existing assessment tools rely on isolated indicators and do not provide a systemic understanding of universities' progression toward desired quality levels. This study applies the project-vector management method as an analytical framework for modeling institutional trajectories in a multidimensional quality space. The research integrates secondary data from the Independent Agency for Quality Assurance in Education with a nationwide survey of 10,718 participants, including students, faculty, administrators, and employers, complemented by 19 expert interviews. The study was conducted in three stages: monitoring and classifying universities by achieved quality level; identifying systemic challenges through survey-based and interview-based SWOT analysis; and applying the project-vector method to calculate resistance coefficients and evaluate movement toward target quality indicators. The results demonstrate substantial variation in university performance across the five IQAA quality components. Survey findings highlight recurring problems such as limited infrastructure capacity, variability in teaching quality, insufficient internationalization opportunities, and inconsistent administrative procedures. SWOT analysis confirms that excessive preventive control, uneven resource allocation, and low levels of autonomy significantly increase systemic resistance. The project-vector calculations reveal that institutions with stronger resource bases and greater managerial flexibility show lower resistance coefficients and more stable trajectories toward the planned quality level. The study concludes that effective quality improvement requires shifting from control-oriented regulation toward data-driven, adaptive governance. The project-vector management method provides a transparent, diagnostic foundation for monitoring, interpreting, and supporting university development trajectories at both institutional and national levels.