What is unique about this course? Rather than teaching students to follow recipes, the curriculum engages students in the process of statistical thinking that allows them to really cook. By keeping the focus of the curriculum exclusively on modeling and simulation the emphasis from the first day in the course is kept on key statistical ideas such as the importance of data collection methods, variation under uncertainty (random sampling vs. random assignment), the ideas of null and alternative models, and a deep understanding of the process statistical inference.
Students are introduced to randomization and bootstrap methods to help draw inferences. The curriculum includes content for inferences regarding means and proportions and for both one- and two-sample comparisons. Students conduct all of the modeling and simulation–including the randomization and bootstrap methods–using the TinkerPlots software.
Because the course uses technology to carry out these simulations, many concepts can be understood from empirical evidence, with less reliance on mathematical theory and computing rules. As a result, some traditional topics such as computing areas under the normal curve, z-scores, and the Central Limit Theorem are less necessary, and have been removed to make more time for concepts at the core of inference.
This course is currently being piloted at both the University of Minnesota and North Carolina State University. We are looking for people who want to become CATALST collaborators by adapting and using this course in one of the following settings:
- A statistics course for pre-service teachers
- A freshman seminar
- A statistical or quantitative literacy course
- A basic introductory statistics course
- An introductory course for students in the sciences
- An online class
- A course that enrolls large numbers of students
What we have to offer: Some stipends to reward you for taking the time to work on adapting and implementing this course, the opportunity to collaborate with an enthusiastic team of statistics educators, the opportunity to create a unique version of the CATALST course that others may use, and the opportunity to collect meaningful data that help you (and us) see how well your students are learning to think statistically and understand the big ideas of statistical inference.
Are you interested? Would you like to join our collaboration? Would you be willing and able to adapt and teach this course next year? Would you be willing to come to a pre-USCOTS meeting in Raleigh, North Carolina (Thursday May 19, 1:30-3:30) to meet with the CATALST team?
If so, please contact Joan Garfield to express your interest. We can share with you our current course outline, syllabus and activities. We are looking for people to keep the bulk of the course intact, but perhaps collapse the second and third unit into one unit and add additional material if needed.
Be sure to check out the CATALST project at: http://www.tc.umn.edu/~catalst/