A New Conceptual Framework for Data Science Education in Interdisciplinary Contexts: The Apian Model

Integrating data science into undergraduate teaching in the United States requires educators to navigate disciplinary boundaries, integrating knowledge in ways that reshape teaching practices and disciplines themselves. While existing models of teaching and learning effectively describe expertise within single disciplines, they do not account for interdisciplinary knowledge movement or the specific challenges of bringing data science into established curricula. The Apian Model addresses this gap using bee behavior as a metaphor: instructors (bees) gather insights (nectar) from diverse curricular domains (flowers), transform them into learning experiences (honey), and disseminate them within and beyond their disciplines (hives). Through incorporation, communication, pollination, and hive splitting dynamics, this model explains how teaching evolves from individual learning to broader disciplinary change, processes especially relevant for navigating the complexity and interdisciplinarity of data science education. The model was developed with data science education in higher education in mind, but future research may establish its broader utility for interdisciplinary education more generally, particularly as a complement to existing models designed for single-discipline instruction.