The ability to interpret and predict from data presented in graphical form is a higher-order thinking skill that is a necessity in our highly technological society. Recent recommendations for the mathematics and science education communities have therefore stressed the importance of engaging learners in real life statistical tasks given in a setting that will promote effective problem solving. Since the small-group setting has been shown to be a fertile environment in which problem solving can occur, we have used that setting for engaging students in data analysis tasks. However, there is a dearth of ideas related to how to assess students' behavior, thinking, and performance in such a setting. The purpose of this chapter is to describe a framework for assessing students' problem solving behaviors on a graph task as they work within a small-group setting.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education