Computational skills by stealth in introductory data science teaching
Published in Teaching Statistics, 2021
Recommended citation: Burr, W., Chevalier, F., Collins, C., Gibbs, A.L., Ng, R., and Wild, C.J., Computational skills by stealth in introductory data science teaching, Teaching Statistics 43 (2021), S34–S51. https://onlinelibrary.wiley.com/doi/10.1111/test.12277
This paper grew out of the work of the International Data Science in Schools Project. We present our proposal for the stealth development of computational skills in introductory data science, using scaffolded exposure to computation and its power. The intent is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data-driven inquiry.