Student dropout is still a crucial issue in many colleges and universities in countries including Indonesia despite various initiatives that have been implemented to reduce its rate. As a preventive effort, many educational institutions continuously monitor several factors that potentially affect student graduation and dropout rates. However, the increasing ratio of student numbers to administrative staff has made manual processes inefficient. This paper presents a novel method to monitor student learning outcomes using a subgraph matching approach to identify students who potentially drop out early or fail to graduate due to low learning achievement. This study emphasizes diagnostic accuracy in identifying students at risk of dropping out as the main focus of the proposed contribution. Performance assessment is directed at the model's ability to accurately distinguish between at-risk and not-at-risk students, particularly through evaluations that emphasize the minority class. This approach strengthens the model's role as the foundation of a more reliable early warning system relevant to supporting academic decision-making. In this research, the academic achievement of each student is represented as a graph that represents the relationship between each student and courses that have been enrolled in the previous semesters. By comparing the learning achievement pattern between each student and those students who have either "graduated" or "dropped out", the students with elevated dropout risk can be identified in a computationally efficient manner.  The experiment findings showed that the proposed method can identify students with elevated dropout risk with 89.47% average accuracy. Graph-based approaches are capable of capturing structural relationships and complex interaction patterns among student activities that cannot be explicitly represented by other models. Although subgraph matching is known to have high computational complexity, the identification process can be accelerated through subgraph pattern constraints, preprocessing strategies, and search optimizations, thereby supporting claims of efficiency both theoretically and practically.