Education & Educational Data Analysis

Statistical methods are now a primary tools for the collection and analysis of data to inform the education, policy, and social sciences. From questionnaire development to the selection of probability samples to the design of social experiments, statisticians at Carnegie Mellon collaborate in the collection of social science data. Faculty and students regularly work with others to develop new methods for analyzing these data and they apply up-to-date methods for drawing inferences from diverse social science data sources ranging from large scale sample surveys to social networks, to educational experiments. A number of statistics graduate students work directly in joint programs bringing statistics to bear on problems in education and public policy.

There are currently no projects for this area of research.

On Teaching Statistical Practice: From Novice to Expert

This article introduces principles of learning based on research in cognitive science that help explain how learning works. We adapt these principles to the teaching of statistical practice and illustrate the application of these principles to the curricular design of a new master’s degree program in applied statistics. We emphasize how these principles can be used not only to improve instruction at the course level but also at the program level.

There are currently no lab groups for this area of research.