To develop competency in science practices, such as data analysis and interpretation, chemistry learners must develop an understanding of what makes an analysis and interpretation "good" (i.e., the criteria for success). One way that individuals extract the criteria for success in a novel situation is through making social comparisons,…

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…

Background: Historically, significant advances in scientific understanding have followed advances in measurement and observation. As the resolving power of an instrument increased, so have gains in the understanding of the phenomena being observed. Modern interactive systems are potentially the new "microscopes" when they are…

Reports on a study designed to develop and validate a framework for characterizing middle school students' thinking across four processes: describing data, organizing and reducing data, representing data, and analyzing and interpreting data. Results indicate that students progress through four levels of thinking within each statistical process.…

Considers individual students' progress as they use tools, discuss data distributions, and interact with teachers and their peers. Suggests that data display tools provide a partial context for discussions but do not constrain the students' interpretations or the way they reason about the data. (Author/MM)