Mapping Student Pathways Using Data Mining Approaches

Session Abstract: This presentation examines student pathways into and through baccalaureate programs across the ten-campus University of Hawaiʻi System, focusing on 8,968 undergraduates enrolled from 2015–2025. Guided by the question, “What course-taking patterns most contribute to upward transfer in food, agriculture, natural resources and human sciences (FANH) majors?”, the study analyzes curricular trajectories leading to successful transfer into bachelor's programs. Drawing on Wang’s STEM transfer research, it uses longitudinal transcript data and data mining to identify course-taking patterns, friction points, and equity gaps. By blending institutional research frameworks with modern analytic methods, the study sheds light on student progression in a transdisciplinary field spanning sustainability, agriculture, environment, and human well-being. It also shows how institutional research can support grants, foster cross-campus collaboration, and guide equity-minded strategies for student success.

Keywords: data mining, pathways, transfer, equity, STEM