Chen, Lujie KarenGillan, JamieDecker, MatthewEteffa, EganMarzan, AnjelicaThai, JustinJewett, Sarah2023-10-262023-10-262023-10-10Chen, Lujie, Jamie Gillan, Matthew Decker, Egan Eteffa, Anjelica Marzan, Justin Thai, and Sarah Jewett. “Embedding Digital Data Storytelling in Introductory Data Science Course: An Inter-Institutional Transdisciplinary Pilot Study.” Journal of Problem Based Learning in Higher Education 11, no. 2 (October 10, 2023): 126–52. https://doi.org/10.54337/ojs.jpblhe.v11i2.7767.https://doi.org/10.54337/ojs.jpblhe.v11i2.7767http://hdl.handle.net/11603/30395With the emergence of data science as an inherently multidisciplinary subject, there is increasing demand for graduates with well-rounded competence in computing, analytics, and communication skills. However, in conventional education systems, computing & quantitative, and communication skills are often taught in different disciplines. Data storytelling is constructing and presenting data stories to highlight the analytical insights to achieve the communication goals to a specific audience. Digital data storytelling leverages digital storytelling techniques and best practices in communication to deliver stories that can be shared in digital formats to a wide audience. In this paper, we describe and reflect on a semester-long project-based learning pilot using Digital Storytelling as a framework to allow students to explore topics themed around human flourishing and sustainability with the end goal of constructing data stories delivered in digital or video format (i.e., Digital Data Storytelling). The pilot work was conducted in an introductory data science course at a 4-year Minority Serving Institution in collaboration with students studying non-STEM disciplines at a partner community college. Our pilot demonstrates the potential benefit of this sustainability-aware Project-Based Learning design in raising students’ awareness of sustainability issues, increasing confidence in cross-disciplinary communication competency, and at the same time deepening their understanding of data science concepts. We further reflect on the significant role of an effective program model as well as challenges and opportunities for building transdisciplinary communication competency to prepare for a diverse data science workforce.27 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Attribution 3.0 Unported (CC BY 3.0 DEED)Text