Building Intrapersonal Competencies in the First-Year Experience: Utilizing Random Forest, Cluster Analysis, and Linear Regression to Identify Students' Strengths and Opportunities for Institutional Improvement

In seeking to close equity gaps within a first-year student seminar course, course designers leveraged emerging research on intrapersonal competency cultivation, known to significantly predict student success across diverse students (NAS, 2018). After re-designing the course to intentionally cultivate specific intrapersonal competencies, researchers set out to explore how well the course closed historical institutional equity gaps as measured by end-of-term GPA. Over four years of data collection and course refinement, traditional regression analysis were useful for informing course improvements that resulted in the closing of some equity gaps. However, students were still being placed on academic probation and certain identities of students were over-represented in academic probation numbers. As such, the team utilized random forest, cluster analysis, and then regression analysis that allowed them to focus improvement efforts on a cluster of students that would have otherwise remained unidentified through traditional analysis measures.