Data Analytics of University Student Records

Understanding the proper navigation of a college curriculum is a daunting task for students, faculty, and staff. Collegiate courses offer enough intellectual challenge without the unnecessary confusion caused by course scheduling issues. Administrative faculty who execute curriculum changes need both quantitative data and empirical evidence to support their notions about which courses are cornerstone. Students require clear understanding of paths through their courses and majors that give them the optimal chance of success. In this work, we re-envision the analysis of student records from several decades by opening up these datasets to new ways of interactivity. We represent curricula through a graph of interconnected courses, studying correlations between student grades. This opens up possibilities for discovering intellectual prerequisites not shown in the course catalog. Extending this, we define a similarity metric for majors within the university, performing hierarchical clustering to reveal structure within this graph of majors not even present within the catalog. Lastly, we seek to show the temporal development of majors as the network grows through time. Through these approaches, our work provides improvements to current methods of viewing and interacting with student records.