Mining Course Groupings based on Academic Performance
Daniel Leeds, Tianyi Zhang, Gary Weiss
Jun 30, 2021 20:40 UTC+2
—
Session PS1
—
Gather Town
Jul 02, 2021 14:10 UTC+2
—
Session PS2
—
Gather Town
Keywords: Graph Mining, Academic Performance, Clustering, Correlation, Educational Data Mining
Abstract:
This study computes the correlation of student grades between pairs of courses in a large university. Course network graphs are then generated, where courses are represented as nodes and courses are connected if they have a high degree of grade correlation. Graph mining and network analysis tools visualize the course networks, identify course clusters and course cliques, and compute informative network statistics. Results are analyzed for pairs of courses and courses grouped by academic department or program of study. Strong course similarity groupings are observed within scientific disciplines, between pre-health courses, and within subfields of computer science. No prior study using this notion of course similarity has been conducted.