Understanding Pedagogical Content Knowledge of Data Science Instructors: An Inaugural Framework (2508.14009v1)

Abstract: As data science emerges as a distinct academic discipline, introductory data science (IDS) courses have also drawn attention to their role in providing foundational knowledge of data science to students. IDS courses not only help students transition to higher education but also expose students to the field, often for the first time. They are often taught by instructors without formal training in data science or pedagogy, creating a unique context for examining their pedagogical content knowledge (PCK). This study explores IDS instructors' PCK, particularly how instructors' varied backgrounds interact with their instructional practices. Employing empirical phenomenological methodology, we conducted semi-structured interviews to understand the nature of their PCK. Comparing instructors' PCK was inherently challenging due to their diverse backgrounds and teaching contexts. Prior experiences played a central role in shaping participants' instructional choices. Their perceptions regarding the goals and rationale for teaching data science reflected three distinct orientations. Instructors also acknowledged students entering IDS courses often brought preconceived notions that shaped their learning experiences. Despite the absence of national guidelines, participants demonstrated notable overlap in foundational IDS content, though some instructors felt less confident with advanced or specialized topics. Additionally, instructors commonly employed formative and summative assessment approaches, though few explicitly labeled their practices using these terms. The findings highlight key components of PCK in IDS and offer insights into supporting instructor development through targeted training and curriculum design. This work contributes to ongoing efforts to build capacity in data science education and expand the scope of PCK research into new interdisciplinary domains.