Teaching Statistics has just published a special issue “Teaching Data Science and Statistics: Re-thinking Learners’ Reasoning with Non-traditional Data”, guest edited by Jennifer Noll, Sibel Kazak, Lucía Zapata-Cardona and Katie Makar.
The special issue addresses some of the open questions in how the field of statistics education may begin to support the teaching and learning of methods for working with non-traditional data.
The articles in this issue focus on new approaches to the teaching and learning of data practices related to messy, complex, or non-traditional data from the youngest learners [1, 2] to secondary learners [3, 4], undergraduate students [5, 6], graduate students, teachers, and researchers [4, 7, 8, 9]. There are two overarching themes in the articles in this special issue: new ways to consider data visualizations in the classroom [2, 3, 7, 4, 8] and new approaches or elements that need to be considered in the teaching and learning of data science practices [1, 5, 6, 9] .
- M in CoMputational thinking: How long does it take to read a book? Teach. Stat. 45 (2023), S30– S39. https://doi.org/10.1111/test.12348 (Open Access) , , and ,
- The possibilities of exploring nontraditional datasets with young children, Teach. Stat. 45 (2023), S22– S29. DOI 10.1111/test.12349 ,
- Students’ approaches to exploring relationships between categorical variables, Teach. Stat. 45 (2023), S52– S66. https://doi.org/10.1111/test.12331 (Open Access) , , , and ,
- Designing a sequence of activities to build reasoning about data and visualization, Teach. Stat. 45 (2023), S80– S92. https://doi.org/10.1111/test.12341 (Open Access) , , , and ,
- How learners produce data from text in classifying clickbait, Teach. Stat. 45 (2023), S93– S103. https://doi.org/10.1111/test.12339 , , , and ,
- Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethics, Teach. Stat. 45 (2023), S5– S21. https://doi.org/10.1111/test.12345 and ,
- Reflections on gaze data in statistics education, Teach. Stat. 45 (2023), S40– S51. https://doi.org/10.1111/test.12340 (Open Access) ,
- What goes before the CART? Introducing classification trees with Arbor and CODAP, Teach. Stat. 45 (2023), S104– S113. DOI 10.1111/test.12347 and ,
- Students’ articulations of uncertainty about big data in an integrated modeling approach learning environment, Teach. Stat. 45 (2023), S67– S79. https://doi.org/10.1111/test.12330 and ,