Computational skills by stealth in introductory data science teaching. (25th June 2021)

• Record Type: Journal Article
• Authors: Burr, Wesley; Chevalier, Fanny; Collins, Christopher; Gibbs, Alison L; Ng, Raymond; Wild, Chris J
• Is Part Of: Teaching statistics. Volume 43(2021)Supplement 1
• Page Start: S34
• Page End: S51
• Publication Date: 2021-06-25
• Subjects: accessibility -- computational thinking -- data science education -- statistical thinking -- statistics education -- teaching statistics; Statistics -- Study and teaching (Elementary) -- Periodicals; Statistics -- Study and teaching (Secondary) -- Periodicals; 373
• DOI: 10.1111/test.12277
• Languages: English
• ISSNs: 0141-982X

Abstract

Abstract: In 2010, Nolan and Temple Lang proposed "integration of computing concepts into statistics curricula at all levels." The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the stealth development of computational skills in students' exposure to introductory data science through careful, scaffolded exposure to computation and its power. Our 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. Reference is made to the computer science and statistics consensus curriculum frameworks the International Data Science in Schools Project (IDSSP) recently published for secondary school data science or introductory tertiary programs, designed to optimize data‐science accessibility.