ASSESSMENT OF CURRICULUM GRAPHS WITH RESPECT TO STUDENT FLOW AND GRADUATION RATES

Universities have been predicting graduation rates for incoming student classes using a multitude of factors. These factors can be broken down into two categories: preinstitutional and institutional. Pre-institutional factors include socioeconomic (race, gender, economic status) and high school performance data (G.P.A, S.A.T). The combination of this data can be incorporated into a statistical model for predicting graduation rates. While this information can give a broad student success estimate to the university, the refinement comes from the institutional factors. These can range from semester grade point averages to the amount of time a student spends at the gym. One particular factor that will be presented and studied in this thesis is curriculum difficulty, defined through features of a curriculum represented as a graph. A curriculum can be presented as a directed graph, with each class as an individual node, and co/prerequisites as the edges between them. Features of these graphs are defined and studied over several comparable universities’ electrical engineering programs. Further, student data at the class level is adapted to create a weighted digraph form which a cumulative curricular difficulty/complexity metric is obtained. The purpose of this tool is to give administration quick access to curricular