Keywords
studio-based learning
shared facilities
data envelopment analysis
queueing theory
educational management
resource efficiency
access fairness
Abstract
This study develops a resource allocation model for studio-based learning environments where students depend on shared spaces, instructor guidance, specialized equipment, and extended practice time. The study is designed to collect data from approximately 40 studio-based courses in 10 higher education institutions, including around 9,600 space-booking records, 5,400 equipment-use logs, 3,200 instructor consultation records, 1,800 course-task records, and 780 student survey responses. Key variables include space utilization rate, equipment idle rate, peak-time congestion, instructor consultation frequency, student access waiting time, course priority level, task completion rate, perceived access fairness, and learning satisfaction. The study applies data envelopment analysis, integer programming, queueing theory, hierarchical regression, and simulation modeling to evaluate how different allocation strategies affect resource efficiency and learner access. It compares first-come-first-served allocation, priority-based allocation, demand-based allocation, and optimization-based allocation. Model performance is measured by utilization efficiency, waiting-time reduction, allocation fairness index, workload balance, and student completion continuity. The innovation of this study lies in combining educational management with operations research methods, offering a quantifiable framework for reducing resource mismatch in studio-based programs and improving equal access to high-demand learning facilities.
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