Predicting the Nexus between Post-Secondary Education Affordability and Student Success: An Application of Network-Based Approaches

The cost of post-secondary education in the U.S. continues to grow faster than salaries and inflation. In fact, the real cost of a college education has climbed almost 30 in the past 10 years and shows no sign of stabilizing in the near future. The economic competitiveness of the country increasingly depends on a skilled workforce with a post-secondary education capable of dealing with the demands of the global market. Thus, college attainment is at the center of producing a skilled workforce, and so it is, post-secondary education affordability. Using the national post-secondary student aid surveyor the year 2003- 2004, which is representative of the entire undergraduate population in the U.S., this study examines the various ways students and families pay for post-secondary education and its subsequent effect on persistence and performance for all groups of students across racial/ethnic and social-economic status lines.We use a spectral clustering algorithm based on normalized cuts to classify students based on their similarities. More specifically, we construct a social network with the students as the nodes of the graph and edge between pair of the students is weighted based on their similarity in attributes. We then obtain three nontrivial smallest of the Laplacian matrix. We use these to perform a k-means clustering in the eigenspace. We were able to establish meaningful clusters by this approach that helps in classifying students based on the relation between their persistence level and conditions of living.