A Data Mining Approach to Understanding Curriculum-Level Factors That Help Students Persist and Graduate

This Research Full Paper describes the analysis of curriculum-level factors that affected the persistence and graduation outcomes of over 4,000 undergraduate students at San Francisco State University. This work addressed four questions: (1) how did the timing of students’ Mathematics courses affect their performance and outcome; (2) whether students who progressed farther through the prescribed foundation course sequences of the university’s long-duration learning community program exhibited higher persistence and graduation rates; (3) what were the most frequently-taken sequences of courses, and whether students who progressed farther through those sequences exhibited higher graduation rates; and (4) whether greater progress was more important than other demographic and academic factors for predicting persistence and graduation. We found that students who took their first non-remedial Math course in the second year showed higher fifth-term and seventh-term persistence than students who took it in the first year. Also, students who progressed farther through their chosen or prescribed sequences consistently exhibited higher persistence and graduation rates. Furthermore, a student’s persistence was a more reliable predictor of graduation than other features. Overall, these findings can potentially inform an institution’s strategies for maximizing persistence and graduation by emphasizing a student’s progress through the curriculum.