Webinars

Technological advances in recent years have changed the possibilities for incorporating non-traditional learning approaches into the classroom. In this webinar we will demonstrate use of a 3-D game, TigerStat, for teaching statistics. In addition to demonstrating the game, we will present the first investigative lab module (lab) developed for teaching simple linear regression in an introductory statistics course. The lab emphasizes statistical thinking and the process of scientific inquiry to students using the game as a part of the data collection effort. The game-based lab presents a research question in the context of a case study and encourages students to follow a complete process of statistical analysis. These labs are designed to 1) foster a sense of engagement, 2) have a low threat of failure early on but create a challenging environment that grows with the students' knowledge, 3) create realistic, adaptable, and straightforward models representing current research in a variety of disciplines, and 4) provide an intrinsic motivation for students to want to learn. The game and lab materials were developed as part of NSF grant TUES DUE #1043814 with co-PI Shonda Kuiper, Grinnell College, and software development by Tietronix Software.

We will briefly review the development and evolution of Stat 303: The Art and Practice of Teaching Statistics, a required year-long course for all entering Ph.D. students in the Department of Statistics at Harvard University. The course started in 2005-2006, and has been revised annually to address students' feedback and evolving goals, as listed in the title. Dr. Meng will talk from his syllabus, which he will also display on the screen. Participants can follow the talk/discussions based on the following handouts. Feel free to make copies for note taking.

This presentation shows how the application of simple statistical methods can reveal to students important insights from climate data. While the popular press is filled with contradictory opinions about climate science, teachers can encourage students to use introductory-level statistics to analyze data for themselves on this important issue in public policy. The detailed example in this presentation addresses the very important topic of the rate of decline of Arctic sea ice. Many climate scientists believe that Arctic sea ice melt is accelerating. The simple data analyses of this paper are meant to encourage students to examine the evidence themselves using tools they learn in their introductory statistics course.

In this webinar, we will provide an overview of goals and methods of curriculum evaluation that are appropriate for use in statistics education projects, share details of newly developed instruments that may be used in evaluation of these projects, and provide an example of evaluation methods used in the CATALST project along with a summary of what was learned in this evaluation. Additional information on the NSF-funded eATLAS (Evaluation and Assessment of Teaching and Learning About Statistics, NSF DUE 1044812 & 1043141) project will be shared regarding collection of national data to use in future evaluations.

Lisa Green & Scott McDaniel, Middle Tennessee State University

If the rate of cancer in your small town is three times the national average, should you be alarmed? A short and simple activity that allocates cancer cases to random locations, using a pair of dice, shows that a rate of 3 or even 4 times the national average is not surprising.

Introduce yourself to the new model being used in a large, introductory statistics course. Technology is creatively leveraged to provide students with rich, flexible learning opportunities, timely instructor feedback, and options for making lecture attendance suitable to their learning style. Experience the new ways students are engaging with lecture content through the use of tablet PCs, interactive polling, and a backchannel. This webinar will give you just a taste of the ideas, but hopefully you will be interested in more.

There is a need for modern, efficient, and engaging pedagogical techniques for enhancing the teaching of probability theory, and its applications, that leave lasting impressions on learners. The Probability Distributome project has developed portable, browser-accessible and extensible resources including: Computing probability and critical values for a wide array of distributions Exploring probability distribution properties and inter-distributional relations Fitting probability distribution models to data Virtual resampling and simulation experiments Integrated data, web-applications and learning-activities We will show some of the Distributome web-resources and discuss best practices for integrating these tools, web-applications, activities and learning materials in probability and statistics curricula.

As part of the Open Learning Initiative (OLI), Carnegie Mellon University was funded to develop a web-based introductory statistics course, openly and freely available to individual online students so they could learn effectively without an instructor. In practice, this course is often used in "blended" mode, i.e., to complement face-to-face classroom instruction. In this webinar, we will demonstrate how students interact with the course and how the different activities were designed to provide pedagogical scaffolding. We will then discuss ways in which instructors have used the online course to support their teaching and provide a demonstration of the Instructor's Learning Dashboard, a tool which continuously provides detailed feedback on students' learning and progress. We will conclude by summarizing a set of studies in which we assessed the online course's effectiveness in blended mode. Unfortunately, this webinar was not recorded due to a technical problem. We apologize.

In this webinar I'll give a nuts-and-bolts description of a fourth year capstone activity for students in statistics programs at the University of Toronto. The statistics students join research students from other disciplines as collaborators. I'll describe what takes place including the nature of the projects and the support provided, how we've structured the course and are evaluating the projects, who are the members of the six distinct groups of individuals at the university who are benefitting from the experience, and why we started the course and organized it the way we did.