Assessing a statistics capstone course against a university’s graduate profile attributes framework
DOI:
https://doi.org/10.52041/iase24.506Abstract
Compulsory capstone courses were introduced in 2019 for all undergraduates in the Faculty of Science in recognition that students required support to transition from being a student of a discipline to a practitioner. These capstone courses required the University of Auckland graduate profile attributes to be assessed. This research aimed to discover how a generic institutional framework could be used and if it provided insights into the statistics discipline. Coursework submitted by two cohorts of students enrolled in the statistics capstone course were examined. This paper demonstrates that the use of a generic framework provided a new lens on statistics capstone course assessment and prompted an awareness of the skills and knowledge needed to improve the transition from university.References
Association of American Colleges and Universities (2009). VALUE Rubrics. Association of American Colleges and Universities. https://www.aacu.org/value-rubrics
Association for Computing Machinery (2018). Code of ethics. Association for Computing Machinery. https://www.acm.org/code-of-ethics
American Statistical Association. (2014). Curriculum guidelines for undergraduate programs in statistical science. American Statistical Association.
American Statistical Association. (2022). Ethical guidelines for statistical practice. American Statistical Association. https://www.amstat.org/your-career/ethical-guidelines-for-statistical-practice
Barnett, R. (2004). Learning for an unknown future. Higher Education Research & Development, 23(3), 247-260. https://doi.org/10.1080/0729436042000235382
Beckman, M. (2019, September 10). Capstone assessment for the undergraduate statistics major. Consortium for the Advancement of Undergraduate Statistics Education (CAUSE). https://www.causeweb.org/cause/webinar/teaching/2019-09
Bilgin, A.A.B., & Petocz, P. (2019, August 13-16). Dealing with the increased assessment workload of work-integrated learning in a capstone unit for an undergraduate major in statistics. Satellite conference of the International Association for Statistics Education (IASE), Kuala Lumpur, Malaysia.
Carroll, S.R., Herczog, E., Hudson, M. et al. (2021a) Operationalizing the CARE and FAIR Principles for Indigenous data futures. Scientific Data, 8, 108. https://doi.org/10.1038/s41597-021-00892-0
Carroll, S., Hudson, M., Walter, M., & Axelsson, P. (2021b). International indigenous data sovereignty interest group. Research Data Alliance. https://www.rd-alliance.org/groups/international- indigenous-data-sovereignty-ig
Cawthorne, R. (2022). Indigenising the Australian University Science Curriculum. Macquarie University. Thesis. https://doi.org/10.25949/21928488.v1
Coates, S., Trudgett, M., & Page, S. (2021). Indigenous higher education sector: The evolution of recognised Indigenous Leaders within Australian Universities. The Australian Journal of Indigenous Education, 50(2), 215-221. https://doi:10.1017/jie.2019.30
Collins, A., Brown, J., & Newman, S. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing and mathematics. In L. Resnick (Ed.), Knowing, learning and instruction: Essays in honor of Robert Glaser (pp. 453-494). Routledge. https://doi.org/10.4324/9781315044408
Gilbert, A., Knewstubb, B., Longley, A., & Kensington-Miller, B. (2018). From invisible to SEEN: A conceptual framework for identifying, developing, and evidencing unassessed graduate attributes. Higher Education Research & Development, 37(7), 1439-1453. https://doi.org/10.1080/07294360.2018.1483903
King, N. (1998). Template analysis. In G. Symon, & C. Cassell (Eds.), Qualitative methods and analysis in organizational research (pp. 118-134). Sage.
Lamb, S., Maire, Q., & Doecke, E. (2017, August). Key skills for the 21st century: An evidence-based review. New South Wales Government. https://education.nsw.gov.au/content/dam/main- education/teaching-and-learning/education-for-a-changing-world/media/documents/Key-Skills- for-the-21st-Century-Executive-Summary.pdf
Lee, H., Mojica, G., Thrasher, E. P., & Baumgartner, P. (2022). Investigating data like a data scientist: Key practices and processes. Statistics Education Research Journal, 21(2), Article 3. https://doi.org/10.52041/serj.v21i2.41
Nicholson, J., Gal, I., & Ridgway, J. (2022). Understanding civic statistics: A conceptual framework and its educational applications. In J. Ridgway (Ed.), Statistics for empowerment and social engagement (pp. 37-66). Springer. https://doi.org/10.1007/978-3-031-20748-8_3
Nilsson, P., Schindler, M., & Bakker, A. (2018). The nature and use of theories in statistics education. In D. Ben-Zvi, K.M. Makar, & J.B. Garfield (Eds.), International handbook of research in statistics education (pp. 359-386). Springer. https://doi.org/10.1007/978-3-319-66195-7_11
Passmore, R. (2024). Assessment of graduate profile attributes in a statistics capstone course. PhD thesis, The University of Auckland, New Zealand. https://hdl.handle.net/2292/70111
Pfannkuch, M., & Wild, C. (2000). Statistical thinking and statistical practice: Themes gleaned from professional statisticians. Statistical Science, 15(2), 132-152. https://doi.org/10.1214/ss/1009212754
ProCivicStat (2018). Engaging civic statistics: A call for action and recommendations. A product of the ProCivicStat Project. International Association for Statistical Education (IASE). https://iase- web.org/islp/pcs/documents/ProCivicStat_Report.pdf
Ridgway, J. (Ed.). (2022). Statistics for empowerment and social engagement: Teaching civic statistics to develop informed citizens. Springer. https://doi.org/10.1007/978-3-031-20748-8
Robley, W., Whittle, S., & Murdoch-Eaton, D. (2005). Mapping generic skills curricula: Outcomes and discussion, Journal of Further and Higher Education, 29(4), 321-330.
Te Mana Raraunga. (2023). Te mana raraunga Māori data sovereignty network. Te Mana Raraunga. https://www.temanararaunga.maori.nz/
Tractenberg, R. (2022). Ten simple rules for integrating ethics into statistics and data science instructions. Open Archive of the Social Sciences (Soc ArX IV). https://doi.org/10.31235/osf.io/z9uej
Whiteford, G., Hunter, J., Jamie, J., Pitson, R., Breckenridge, D., Elders, Y., Vemulpad, S.,Harrington, D., & Jamie, I. (2017). The river of learning: Building relationships in a university, school and community Indigenous widening participation collaboration. Higher Education Research and Development, 36(7), 1490–1502. https://doi.org/10.1080/07294360.2017.1325845
Wild, C., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223-248. https://doi.org/10.1111/j.1751-5823.1999.tb00442.x
