Teaching Data Science and Statistics: Re-thinking Learners’ Reasoning with Non-traditional Data

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] .

  1. Fry, K.Makar, K., and Hillman, J.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)
  2. Zapata-Cardona, L.The possibilities of exploring nontraditional datasets with young childrenTeach. Stat. 45 (2023), S22– S29. DOI 10.1111/test.12349
  3. Higgins, T.Mokros, J.Rubin, A., and Sagrans, J.Students’ approaches to exploring relationships between categorical variablesTeach. Stat. 45 (2023), S52– S66https://doi.org/10.1111/test.12331 (Open Access)
  4. Rao, V. N. V.Legacy, C.Zieffler, A., and delMas, R.Designing a sequence of activities to build reasoning about data and visualizationTeach. Stat. 45 (2023), S80– S92https://doi.org/10.1111/test.12341 (Open Access)
  5. Horton, N. J.Chao, J.Palmer, P., and Finzer, W.How learners produce data from text in classifying clickbaitTeach. Stat. 45 (2023), S93– S103https://doi.org/10.1111/test.12339
  6. Noll, J. and Tackett, M.Insights from DataFest point to new opportunities for undergraduate statistics courses: Team collaborations, designing research questions, and data ethicsTeach. Stat. 45 (2023), S5– S21https://doi.org/10.1111/test.12345
  7. Boels, L.Reflections on gaze data in statistics educationTeach. Stat. 45 (2023), S40– S51https://doi.org/10.1111/test.12340 (Open Access)
  8. Erickson, T. and Engel, J.What goes before the CART? Introducing classification trees with Arbor and CODAPTeach. Stat. 45 (2023), S104– S113. DOI 10.1111/test.12347
  9. Gafny, R. and Ben-Zvi, D.Students’ articulations of uncertainty about big data in an integrated modeling approach learning environmentTeach. Stat. 45 (2023), S67– S79https://doi.org/10.1111/test.12330