Understanding Pedagogical Content Knowledge of Introductory Data Science Instructors: An Inaugural Framework

Statistics > Other Statistics [Submitted on 19 Aug 2025 (v1), last revised 17 Jun 2026 (this version, v3)] Title:Understanding Pedagogical Content Knowledge of Introductory Data Science Instructors: An Inaugural Framework View PDFAbstract:As data science emerges as a distinct academic discipline, introductory data science (IDS) courses play a key role in shaping students foundational understanding. Often taught by instructors without formal training in data science or pedagogy, these courses present a unique and globally relevant context for examining pedagogical content knowledge (PCK). Drawing on semi-structured interviews with 14 IDS instructors and their course syllabi, this study explores how IDS instructors describe and make sense of their teaching practices, which are analyzed through the lens of PCK. The findings highlight key components of PCK about IDS and offer insights into supporting instructor development. This work contributes to expanding the scope of PCK research into new interdisciplinary domains and ongoing global efforts to build capacity in data science education. It could serve as a starting point for developing a PCK framework specific to IDS. Submission history From: Sinem Demirci Dr [view email][v1] Tue, 19 Aug 2025 17:15:14 UTC (482 KB) [v2] Mon, 25 Aug 2025 20:27:53 UTC (1,039 KB) [v3] Wed, 17 Jun 2026 23:15:28 UTC (983 KB) References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.