Implementation of BFS and DFS Algorithms to Support Operational Decisions in Higher Education Information Systems

Abstract

In higher education, effective academic planning plays a crucial role in supporting student success and institutional efficiency. This study explores the implementation of Breadth First Search (BFS) and Depth First Search (DFS) algorithms to support operational decision-making in academic information systems. These graph-based algorithms are utilized to model curriculum structures, particularly in identifying optimal paths between prerequisite courses. BFS is applied to determine the shortest academic paths, while DFS is employed to explore deeper learning trajectories. Using real curriculum data from a university information system, both algorithms were tested to simulate course dependency mapping. The results demonstrate that BFS efficiently identifies minimum-step paths, which are helpful for academic advisors and students in planning semester schedules. On the other hand, DFS offers comprehensive insight into all possible course progressions. This research highlights the potential of algorithmic approaches to enhance the decision-making process in academic planning. Future developments may include integration with recommendation systems and predictive analytics to further support personalized learning paths.