By Jennifer Broatch (Arizona State University)
Information
As data science becomes increasingly integral across disciplines, understanding how it is taught at the introductory level is essential for shaping effective and inclusive curricula. This NSF sponsored project (DUE- 2423026) explores the current national landscape of introductory data science education, identifying the core competencies emphasized in existing courses and examining how students engage with and develop data literacy across diverse learning environments. Using a mixed-methods approach, the study integrates quantitative survey data from instructors utilizing the MASDER developed survey (SDSattitudes.com), qualitative insights from semi-structured interviews, and content analysis of syllabi to provide a comprehensive picture of data science education for non-majors.
The study focuses on institutions offering introductory data science courses for a general audience, where course structures, student backgrounds, and learning objectives vary widely. By analyzing curriculum design and competency expectations, this research highlights trends, challenges, and gaps in how foundational data science concepts are introduced. Initial findings will discuss differences in instructional approaches, the integration of computational tools, and how instructors balance statistical reasoning, coding, and real-world applications.
This poster will share early insights and invite attendees to contribute to ongoing data collection through compensated instructor surveys and interviews. Engaging with educators and researchers at USCOTS 2025 will help refine our understanding of effective teaching strategies for fostering data literacy. Participants are encouraged to join the discussion and help shape a research-informed framework for introductory data science education that supports students from all academic backgrounds.