Choose your own adventure: Experiencing research through first-year group projects in data science



collaborative learning, authentic learning, undergraduate research, data stories


Traditionally, an undergraduate’s first experience of statistics has been intentionally sanitised, with well-defined research questions on very clean or contrived datasets. As a result, the learning experience is not only dull, but students are quarantined from the authentic research experience of problem solving with data. Responding to the seminal Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendations (Carver et al., 2016; Franklin et al., 2007), our new data science curriculum allows thousands of students to experience the messy but exhilarating process of independent data discovery from day one. Through the introduction of an integrated series of collaborative group data-projects, students experience genuine data science research within a scaffolded environment that supports their learning experience. Moving through different data types (sourced data, survey data, client data), students choose their own adventure by constructing their own research questions and then presenting their unique findings to their tutor and peers for interrogation. The use of reproducible RMarkdown documents enables collaboration and the production of professional reports, consistent with any research environment. Though challenging, students report their research experience as satisfying and motivating for their statistics study, as well as transferring to other domains. References Carver, R., Everson, M., Gabrosek, J., Horton, N., Lock, R., Mocko, M., . . . Witmer, J. (2016). Guidelines for assessment and instruction in statistics education: College Report 2016. Retrieved from Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., & Scheaffer, R. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report. Alexandria: American Statistical Association.

Author Biographies

Diana Warren, The University of Sydney

Di was awarded the Faculty Of Science Outstanding Teaching Award in both 2017 and 2018, for inspiring lecturing and continual educational innovation focused on increasing student motivation and engagement, particularly in very large courses. She is responsible for overseeing the curriculum development of first year Data Science and Statistics courses, with increasing emphasis on current data stories, problem-based learning and student's ability to write and present collaborative reports.

Samantha Louise Clarke, The University of Sydney

Samantha is a marine/engineering geologist by training, researcher and academic developer by day, and passionate about the ongoing pursuit of learning and education. Her work spans educational technology, student engagement, pedagogical research, organisational leadership, and professional development. She is currently a Lecturer in Academic Development and Leadership in the Educational Innovation team in the DVC (Education) Portfolio and an Honorary Associate in the School of Geosciences, both at the University of Sydney.