This paper describes algorithmic decision support that facilitates recommendation of course schedules personalized to the background and interests of a given student. More specifically, recommendations are made with prioritized consideration of four categories of information: (1) degree requirements, (2) student interests, (3) student performance, and (4) time-to-degree. All four categories of information may not be available for a given student. The algorithm generates personalized recommendations by constructing a graph from degree requirements, identifying critical paths in the graph, and placing such paths within a course schedule. We describe the implementation of the algorithm in the context of PERCEPOLIS, a Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support, a framework constructed in our earlier work on personalized learning.