Embedding Digital Data Storytelling in Introductory Data Science Course
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How to Cite

Chen, L., Gillan, J., Decker, M., Eteffa, E., Marzan, A., Thai, J., & Jewett, S. (2023). Embedding Digital Data Storytelling in Introductory Data Science Course: An Inter-Institutional Transdisciplinary Pilot Study. Journal of Problem Based Learning in Higher Education, 11(2), 126–152. https://doi.org/10.54337/ojs.jpblhe.v11i2.7767

Abstract

With 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.

https://doi.org/10.54337/ojs.jpblhe.v11i2.7767
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References

Adhikari, A., DeNero, J., & Jordan, M. I. (2021). Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley. Harvard Data Science Review. https://doi.org/10.1162/99608f92.cb0fa8d2

Allen, D. E., Donham, R. S., & Bernhardt, S. A. (2011). Problem-based learning. New Directions for Teaching and Learning, 2011(128), 21–29. https://doi.org/10.1002/tl.465

Allen, G. I. (2021). Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone Program. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 516–522. https://doi.org/10.1145/3408877.3432536

BBC (Director). (2010, November 26). Hans Rosling’s 200 Countries, 200 Years, 4 Minutes—The Joy of Stats—BBC Four. https://www.youtube.com/watch?v=jbkSRLYSojo

Bhargava, R. (2017). Data Storytelling Studio: Climate Change | Media Arts and Sciences. MIT OpenCourseWare. https://ocw.mit.edu/courses/cms-631-data-storytelling-studio-climate-change-spring-2017/

Bietti, L. M., Tilston, O., & Bangerter, A. (2019). Storytelling as Adaptive Collective Sensemaking. Topics in Cognitive Science, 11(4), 710–732. https://doi.org/10.1111/tops.12358

Brace, A., Finkelsten, B., & Diadrey-Anne, S. (2015). Evaluating the effectiveness of creating digital stories in a college classroom to promote a healthy food system. Food Studies, 6(1), 15–26.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Casola, L. (Ed.). (2020). Roundtable on Data Science Postsecondary Education: A Compilation of Meeting Highlights. National Academies Press. https://doi.org/10.17226/25804

Chan, B. S. K., Churchill, D., & Chiu, T. K. F. (2017). Digital Literacy Learning In Higher Education Through Digital Storytelling Approach. Journal of International Education Research (JIER), 13(1), Article 1. https://doi.org/10.19030/jier.v13i1.9907

Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable Agents and the Protégé Effect: Increasing the Effort Towards Learning. Journal of Science Education and Technology, 18(4), 334–352. https://doi.org/10.1007/s10956-009-9180-4

Cohn, N. (2013). Visual Narrative Structure. Cognitive Science, 37(3), 413–452. https://doi.org/10.1111/cogs.12016

Donoghue, T., Voytek, B., & Ellis, S. E. (2021). Teaching Creative and Practical Data Science at Scale. Journal of Statistics and Data Science Education, 29(sup1), S27–S39. https://doi.org/10.1080/10691898.2020.1860725

Duran, D. (2017). Learning-by-teaching. Evidence and implications as a pedagogical mechanism. Innovations in Education and Teaching International, 54(5), 476–484. https://doi.org/10.1080/14703297.2016.1156011

Dykes, B. (2019). Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals. John Wiley & Sons.

Farahi, A., & Stroud, J. C. (2018). The Michigan Data Science Team: A Data Science Education Program with Significant Social Impact. 2018 IEEE Data Science Workshop (DSW), 120–124. https://doi.org/10.1109/DSW.2018.8439915

Fisler, K. (2021). Leveraging Data Science and Social-Impact Analysis to Broaden Participation in Introductory Computer Science Courses. Gesellschaft für Informatik e.V. http://dl.gi.de/handle/20.500.12116/37014

Ghani, R. (2018). Data Science for Social Good and Public Policy: Examples, Opportunities, and Challenges. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 3. https://doi.org/10.1145/3209978.3210231

Halwani, M. A., Amirkiaee, S. Y., Evangelopoulos, N., & Prybutok, V. (2021). Job qualifications study for data science and big data professions. Information Technology & People, 35(2), 510–525. https://doi.org/10.1108/ITP-04-2020-0201

Henke, N., Bughin, J., Manyika, J., Chui, M., Saleh, T., Wiseman, B. & Sethupathy, G. (2016). The Age of Analytics: Competing in a Data-Driven World. McKinsey Global Institute. https://www.cosmeticinnovation.com.br/wp-content/uploads/2017/01/MGI-The-Age-of-Analytics-Full-report.pdf

Herranen, J., & Aksela, M. (2019). Student-question-based inquiry in science education. Studies in Science Education, 55(1), 1–36. https://doi.org/10.1080/03057267.2019.1658059

Hildreth, L. A., Miley, M., Strickland, E., & Swisher, J. (2022). Writing Workshops to Foster Written Communication Skills in Statistics Graduate Students. Journal of Statistics and Data Science Education, 0(0), 1–10. https://doi.org/10.1080/26939169.2022.2138800

Kearns, M., & Roth, A. (2019). The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Oxford University Press. https://www.google.com/books/edition/The_Ethical_Algorithm/z5OzDwAAQBAJ?hl=en&gbpv=1&dq=ethical+algorithm&pg=PP1&printsec=frontcove r

Knaflic, C. N. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals. John Wiley & Sons.

Kosara, R., & Mackinlay, J. (2013). Storytelling: The Next Step for Visualization. Computer, 46(5), 44–50. https://doi.org/10.1109/MC.2013.36

Lambert, J. (2018). Digital Storytelling: Capturing Lives, Creating Community. Routledge.

Lewis, A., & Stoyanovich, J. (2021). Teaching Responsible Data Science: Charting New Pedagogical Territory. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-021-00241-7

Liguori, A. (2020). Unlocking Contested Stories and Grassroots Knowledge. In P. P. Trifonas (Ed.), Handbook of Theory and Research in Cultural Studies and Education (pp. 465–479). Springer International Publishing. https://doi.org/10.1007/978-3-319-56988-8_35

McDonald, N., Akinsiku, A., Hunter-Cevera, J., Sanchez, M., Kephart, K., Berczynski, M., & Mentis, H. M. (2022). Responsible Computing: A Longitudinal Study of a Peer-led Ethics Learning Framework. ACM Transactions on Computing Education, 22(4), 47:1-47:21. https://doi.org/10.1145/3469130

Monroe-White, T., Wright, B., Hulsey, W., Kushins, E., & Hord, A. (2023). Establishing a Data Science for Good Ecosystem: The Case of ATLytiCS. The Journal of the Southern Association for Information Systems, 10(1), 1–19. https://doi.org/doi:10.17705/3JSIS.00029

National Science Foundation. (2021). The STEM Labor Force of Today: Scientists, Engineers, and Skilled Technical Workers. https://ncses.nsf.gov/pubs/nsb20212

Nolan, D., & Stoudt, S. (2021). Communicating with Data: The Art of Writing for Data Science. Oxford University Press.

Norman, D. A. (2023). Design for a Better World: Meaningful, Sustainable, Humanity Centered. MIT Press.

O’Neil, C. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.

P. M. Ribeiro, S. (2016). Developing intercultural awareness using digital storytelling. Language and Intercultural Communication, 16(1), 69–82. https://doi.org/10.1080/14708477.2015.1113752

Rambe, P., & Mlambo, S. (2014). Using digital storytelling to externalise personal knowledge of research processes: The case of a Knowledge Audio repository. The Internet and Higher Education, 22, 11–23. https://doi.org/10.1016/j.iheduc.2014.04.002

Risam, R. (2018). New Digital Worlds: Postcolonial Digital Humanities in Theory, Praxis, and Pedagogy. Northwestern University Press.

Ryoo, J. J., Morris, A., & Margolis, J. (2021). “What Happens to the Raspado man in a Cash-free Society?”: Teaching and Learning Socially Responsible Computing. ACM Transactions on Computing Education, 21(4), 31:1-31:28. https://doi.org/10.1145/3453653

Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. van der, Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. de, … Clopath, C. (2020). AI for social good: Unlocking the opportunity for positive impact. Nature Communications, 11(1), Article 1. https://doi.org/10.1038/s41467-020-15871-z

Topping, K. J. (2005). Trends in Peer Learning. Educational Psychology, 25(6), 631–645. https://doi.org/10.1080/01443410500345172

United Nations. (2015). Transforming our World: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/publications/transforming-our-world-2030-agenda-sustainable-development-17981

VanderWeele, T. J. (2017). On the promotion of human flourishing. Proceedings of the National Academy of Sciences, 114(31), 8148–8156. https://doi.org/10.1073/pnas.1702996114

Williams, J. D., Lopez, D., Shafto, P., & Lee, K. (2019). Technological Workforce and Its Impact on Algorithmic Justice in Politics. Customer Needs and Solutions, 6(3), 84–91. https://doi.org/10.1007/s40547-019-00103-3

Willis, A., Charlton, P., & Hirst, T. (2020). Developing Students’ Written Communication Skills with Jupyter Notebooks. Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 1089–1095. https://doi.org/10.1145/3328778.3366927

Wu, J., & Chen, D.-T. V. (2020). A systematic review of educational digital storytelling. Computers & Education, 147, 103786. https://doi.org/10.1016/j.compedu.2019.103786

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