The Sydney data stories: Towards an inclusive, interdisciplinary approach to large, diverse, first year cohorts
DOI:
https://doi.org/10.52041/iase24.502Abstract
While first-year undergraduate cohorts in data science tend to be increasingly large and diverse, the approach to teaching can be somewhat siloed and not capitalize on the wealth of data stories across an institution. What could a more inclusive approach look like? How can we leverage the interdisciplinary nature of data science to design a curriculum which engages students from many different fields and backgrounds? Our study focuses on the Sydney Data Stories, which is a large, collaborative project across the University of Sydney. Colleagues across the university brought their stories into the lecture theatre, showcasing data and their insights from their field. We outline the storytelling shape of the curriculum, and then consider three case studies, with findings from student data.
References
Advanced Analytics Planning and Enterprise Data. (2024). DATA1001 student data [Unpublished raw data]. University of Sydney.
Chick, H. L. (2007). Teaching and learning by example. Mathematics: Essential research, essential practice, 1, 3-21. https://files.eric.ed.gov/fulltext/ED503746.pdf
Chick, H., & Pierce, R. (2010). Helping teachers to make effective use of real-world examples in statistics In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the Eighth International Conference on Teaching Statistics (ICOTS8), International Statistical Institute. https://iase-web.org/documents/papers/icots8/ICOTS8_2F2_CHICK.pdf?1402524969
Conway, D. (2010, September 30) The data science Venn diagram. Drewconway.com. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
da Ponte, J. P., & Noll, J. (2018). Building capacity in statistics teacher education. In D. Ben-Zvi, K. Makar, & J. Garfield (Eds.), International handbook of research in statistics education. Springer. https://doi.org/10.1007/978-3-319-66195-7_14
Evans, R. D., & Evans, G. E. (1989). Cognitive mechanisms in learning from metaphors. The Journal of Experimental Education, 58(1), 5-19. https://doi.org/10.1080/00220973.1989.10806518
GAISE Report ASA Revision Committee. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report: A pre-K–12 curriculum framework. American Statistical Association. https://www.amstat.org/asa/files/pdfs/gaise/gaiseprek-12_full.pdf
GAISE College Report ASA Revision Committee. (2016). Guidelines for assessment and instruction in statistics education College Report 2016. American Statistical Association. https://www.amstat.org/asa/files/pdfs/GAISE/GaiseCollege_Full.pdf
Gould, R. (2010). Statistics and the modern student. International Statistical Review, 78(2), 297-315. https://doi.org/10.1111/j.1751-5823.2010.00117.x
O’Donnell, M. (2015). Curriculum narratives: Learning as transition, transition as learning. In STARS: Students Transitions Achievement Retention & Success 2015 proceedings. https://www.unistars.org/papers/STARS2015/09 D.pdf
Pfannkuch, M., Regan, M., Wild, C., & Horton, N. J. (2010). Telling data stories: Essential dialogues for comparative reasoning. Journal of Statistics Education, 18(1).
Warren, D. (2022). Mobilising the student's voice in Data Science education: the Great Barrier Reef data project. In S. A. Peters, L. Zapata-Cardona, F. Bonafini, & A. Fan (Eds.), Bridging the gap: Empowering & educating today’s learners in statistics. Proceedings of the 11th International Conference on Teaching Statistics (ICOTS11), International Association for Statistical Education.
Warren, D., & Clarke, S. (2019). Choose your own adventure: Experiencing research through first-year group projects in data science. ACSME, Sydney: The University of Sydney. https://openjournals.library.sydney.edu.au/IISME/article/view/13477
Warren, D., Tarr, G., & Patrick, E. (in press). Promoting excellence and growth in data science education: Developing a mentoring ecosystem. In 5th International Conference for Mathematics Education proceedings.
