Ordinarily, when a student plays a game on a computer, a great deal of data are generated, but never used. This paper describes a technological innovation: games designed for learning mathematics or statistics concepts in which success requires data analysis. These “Data Games” are small-scale, short, web-based games, embedded in a data analysis environment, suitable for  students in about year 7 onwards, and in teacher preparation. We discuss design for the games themselves, curriculum and assessment issues, and connections to research.

The authors’ work to develop capabilities for getting data into the data analysis software Fathom™ is described. Heuristics of detecting data on a web page allow drag and drop of a URL into a document. A collaboration with the Minnesota Population Center makes possible sampling from census microdata from 1850 through 2000. With direct support for Vernier sensors, students can build a model during the process of realtime data collection. Finally, a survey capability makes it easy for teachers and students to create simple data entry forms hosted on a web site such that the collated data is instantly downloadable for data analysis in Fathom. By taking some of the drudgery out of gathering data, these capabilities carry implications for teaching and curriculum development; namely that students should have experience throughout their learning with data that they individually have chosen to explore. It is argued that the skills they gain by engaging in exploratory data analysis with self-chosen and self-generated data are critically important in our data-driven society and not yet adequately supported in K–14 learning.

When experienced analysts explore data in a rich environment, they often transform the dataset. For example, they may choose to group or filter data, calculate new variables and summary measures, or reorganize a dataset by changing its structure or merging it with other information. Such actions background, highlight, or even fundamentally change particular features of the data, allowing different types of questions to be explored. We call these actions data moves. In this paper, we argue that paying explicit attention to data moves, as well as their purposes and consequences, is necessary for educators to support student learning about data. This is especially needed in an era when students are expected to develop critical literacy around data and engage in purposeful, self-directed exploration of large and often complex datasets.