Creating Data Science Pathways for STEM Student Success

Annie Han
Professor of Mathematics
Borough of Manhattan Community College (BMCC), The City University of New York

Borough of Manhattan Community College’s (BMCC) Data Science Pathway will advance the goals of the NSF’s Division for Undergraduate Education (DUE) Program by developing and accelerating a multi-faceted STEM (pathway) model which will; expand options for underrepresented minorities, contextualize course content, reduce attrition in lower-division gateway courses and facilitate senior college transfer. Research indicates that the first and second year of college for most students are critical points for those transitioning into related STEM careers. While data suggests a high percentage leave their intended STEM majors during this time, the rates for women and Latinos are among the highest. Studies indicate that STEM attrition is not due to students’ performance or attitude; instead is closely linked to an early loss of interest in science, culturally unresponsive instruction, insufficient information about STEM-related careers, and a sense of being overwhelmed by the pace and workload load associated with STEM curricula. Evidence suggests that guided pathway programs, which are tightly and consciously structured, may serve as a promising strategy for addressing these challenges and accelerating student success. While rigorous research on pathways is just beginning to emerge, preliminary findings document impressive outcomes for pathway models, with three-year graduation rates more than double (50.3%) that of the community colleges (21.6%) nationally.The BMCC Data Science Pathway represents a major undertaking to advance and improve STEM education for students enrolled in the college’s Math program, the vast majority of whom are URMs. The project aims to serve the national interest by developing and implementing a data science program through a multilayered approach used as a model for other postsecondary institutions. The model will contextualize mathematics course content, reduce attrition in lower-division gateway courses, and facilitate senior college transfer. The resulting data science program will address the educational achievement gap among groups traditionally underrepresented in STEM. The new data science program will promote structural systemic reforms by integrating support services within a sustainable, institutional framework. An additional focus will be on curricular reforms through the development of data science content. Curriculum development will involve integrating data science concepts into five existing mathematics courses and two new/redesigned data science courses. These efforts aim to contribute to students’ data literacy by enhancing their conceptual understanding, integrating the use of real-life data, and fostering active learning.This work will generate evidence to improve the understanding of how a guided pathways model can help students successfully transition from lower-division to upper-division coursework in data science. Qualitative and quantitative data will be collected from multiple sources to respond to the research questions:• How will the new data science program promote systemic structural reforms by integrating support services within a sustainable, institutional framework?


Glenn Miller, BMCC-CUNY, New York; Elisabeth Jaffe, BMCC-CUNY, New York; Jorge Florez, BMCC-CUNY, New York; Stephen Featherstonhaugh, BMCC-CUNY, New York; Oleg Muzician, BMCC-CUNY, New York