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They also observed that spending time having teachers move from data visualizations to data tables is also challenging, but may be a fruitful approach supporting better understanding of the relationships between data and visualizations. Erickson and Engel [[5]] utilize fairly traditional data; however, they explore these data through nontraditional forms of data visualization. The papers in this issue are an important starting point for considering how statistics and data science educators can begin the hard work of preparing students for a more complex society, one driven by data (real and fake) and computing technologies. Traditional statistics education has focused on data from random samples and has capitalized on knowledge about a sample to understand an unknown population. [Extracted from the article]

16th Conference of The Mathematics Education for the Future Project: Building on the Past to Prepare for the Future, King's College, Cambridge University, UK, Aug 8-13, 2022 B We now have more than 180 registrations and invite last-minute papers on innovative ideas in mathematics, statistics, science and computer education for presentation at the conference and publication in the printed ISBN proceedings. b B Our conferences are renowned for their friendly and productive atmosphere, and attract many of the I movers and shakers i in education world-wide. b B This conference will commemorate 60 years of global innovation and promises to be a special I once in a lifetime experience i ! b B For full details, including the guidelines for papers, please email us at b rogersona8@gmail.com, B Alan Rogerson and Jasia Morska b B Conference Organisers b. [Extracted from the article]

In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on "Data and data detectives with CODAP" in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data. [ABSTRACT FROM AUTHOR]

This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary‐school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's "Arbor" plug‐in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu‐based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning. [ABSTRACT FROM AUTHOR]