This article describes an activity and resource from The New York Times that can be used to help learners cultivate critical statistical literacy. Critical statistical literacy involves understanding, interpreting, and questioning statistical information to make informed decisions (Casey et al., 2023; Franklin et al., 2015; Weiland, 2017). It is a…

School science is often very different from "real world" science. One important difference, and possibly the main one, is that in school science the relationships between variables have often been sanitized -- essentially "cleaned up" -- so that there is very little (and often no) variation in the data from the relationship…

Authentic, "messy data" contain variability that comes from many sources, such as natural variation in nature, chance occurrences during research, and human error. It is this messiness that both deters potential users of authentic data and gives data the power to create unique learning opportunities that reveal the nature of science…

Given growing interest in K-12 data and data science education, new approaches are needed to help students develop robust understandings of and familiarity with data. The model of the "quantified self"--in which data about one's own activities are collected and made into objects of study--provides inspiration for one such approach. By…

Learning about people from long ago and far away poses a challenge for students because such people seem so distant and different. The lack of easily comprehensible text-based primary sources compounds this problem. Using a built environment as a primary source makes people from the distant past more accessible, concrete and exciting. Broadly…

Online environments can cultivate what have been referred to as 21st century skills and capabilities, as youth contribute, pursue, share, and interact around work and ideas. Such environments also hold great potential for addressing digital divides related to the development of such skills by connecting youth in areas with fewer resources and…