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Multiple stakeholders are interested in measuring undergraduate student success in college across academic fields. Different metrics might appeal to different stakeholders. Some metrics such as the fraction of first-time, full-time students who start in the fall who graduate within six years, the graduation rate, are federally mandated by the U.S. Department of Education, Integrated Postsecondary Education Data System (IPEDS). We argue that this calculation of graduation rate is inherently problematic because it excludes up to 60% of students who transfer into an institution, enroll part-time, or enroll in terms other than the fall. By expanding the starters definition, we propose a graduation rate definition that includes conventionally excluded students and provides information on progression in a specific program. Stickiness is an even more-inclusive alternative, measuring a program’s success in graduating all undergraduates ever enrolled in the program. In this work, programs are grouped into six academic fields: Arts and Humanities, Business, Engineering, Other, Social Sciences, and STM (Science, Technology, and Mathematics. Stickiness is the percentage of students who ever enroll in an academic field that graduate in the same field. We use the Multiple Institution Dataset for Investigating Engineering Longitudinal Development (MIDFIELD) 2023 which contains unit-record data for over 2 million individual students at 19 institutions. For the academic fields studied, Engineering has the highest graduation rate and third highest stickiness. Social Sciences and Business also have higher graduation rates and stickiness than the other fields. We also track the relative fraction of students migrating to and from each academic field. This paper continues our work to derive better metrics for understanding student success. 

Pipeline and pathways models influence persistence metrics used to study how students navigate engineering education.

This study presents pipeline, pathways, and ecosystem models and their associated metrics, compares and contrasts these models using an intersectional approach to explore persistence, and advocates for use of an ecosystem model.

This study presents a quantitative perspective of engineering student outcomes disaggregated by discipline, race/ethnicity, and sex. It includes 111,925 engineering students from 11 U.S. universities, including first‐time‐in‐college and transfer students who ever majored in the most common engineering disciplines: chemical, civil, electrical, industrial, and mechanical engineering. Contemporary data visualization methods are used to display quantitative data and clarify their complexity.

This work captures the intersectionality of race/ethnicity, sex, and discipline with metrics that are new or little used, such as stickiness (retention by a discipline), migrator graduation rate, and migration yield (attraction of a discipline). Using these metrics, we uncover information about the success of students who migrate between and among the top five engineering disciplines.

Stickiness, migrator graduation rates, and migration yield metrics coupled with contemporary data visualization methods provide insights into the student experience not afforded by the conventional pipeline and pathways models. Considering engineering education as an ecosystem tells stories of complexity and nuance, opening possibilities for new research.