The agenda for this Stat Chat is as follows:
- 6:00 - 6:30, Dinner (and warm up activity to introduce the CATALST philosophy)
- 6:30 - 7:00, Journal Club: The Introductory Statistics Course: A Ptolemaic Curriculum?
- 7:00 - 8:00, Main Event: The CATALST Course (Joan Garfield, Robert delMas, Laura Le, Rebekah Isaak, Laura Ziegler, Andy Zieffler)
During this month's journal club (see below), we will discuss George Cobb's paper The Introductory Statistics Course: A Ptolemaic Curriculum? This paper was the inspiration for the CATALST course, which will be the topic of this month's main event. CATALST is an NSF-funded project that has created a curriculum designed to develop students’ statistical thinking and appreciation of statistics through a focus on modeling, simulation and inference. The CATALST curriculum is currently being taught in several sections of undergraduate-level statistics at the University of Minnesota and at North Carolina State.
We will share an overview of the curriculum, as well as a sample of class activities used in the course. Specifically, an activity used to introduce the randomization test for group inferences will be shared. The software TinkerPlots™, which is used by students in the course to conduct the modeling and simulation, will also be demonstrated.
PLEASE RSVP to Danny Kaplan so that we can plan sensibly for dinner. As always, last-minute deciders and guests are welcome.
Journal club was introduced at Stat Chat during the 2009-2010 academic year as a venue for discussing articles, books, etc. with other statistics educators. The following online resources are provided for this month's discussion of The Introductory Statistics Course: A Ptolemaic Curriculum?
- The Introductory Statistics Course: A Ptolemaic Curriculum? by George Cobb [Read Article]
Some discussion questions:
- In the article, George posits that randomization-based inference deserves to be at the core of every introductory course. Do you agree? Why or why not?
- If someone decides to teach using randomization methods, is it necessary to still teach classical methods (e.g., the t-test) in addition to randomization methods? Or can these be introduced as an aside?
- George argues that technology has freed us to simplify our curriculum. Is this true? Or has it merely shifted the focus? Has it in fact made things more complex because we now have an obligation to teach computing as well?
- What should the balance be between teaching statistics and teaching computing?