Marsha Lovett, Carnegie Mellon University
Tuesday, January 12, 2010 - 2:00pm
In Statistics as in many disciplines, students need to learn about complex concepts and dynamically changing processes. How can instructors help their students begin to "see" these complex topics the way experts do, and are there tools that can help? In this webinar, I will review key findings on how computer visualizations and simulations can best support student learning and then take those findings to generate effective strategies for teaching with simulations and visualizations.
Ron Wasserstein, American Statistical Association
Tuesday, November 10, 2009 - 2:00pm
Statistics educators are keenly aware of the value of using real data to help students see the relevance and applicability of statistics. The federal statistical agencies have invested in significant efforts to make data accessible and available. In this webinar, Ron Wasserstein will point you to these resources, discussing their uses and limitations.
Brenda Gunderson, University of Michigan
Tuesday, September 8, 2009 - 2:00pm
Many introductory Statistics courses consist of two main components: lecture sections and computer laboratory sections. In the computer labs, students often review fundamental course concepts, learn to analyze data using statistical software, and practice applying their knowledge to real world scenarios. Lab time could be better utilized if students arrived with 1) prior exposure to the core statistical ideas, and 2) a basic familiarity with the statistical software package. To achieve these objectives, PreLabs have been integrated into an introductory statistics course. A simple screen capture software (Jing) was used to create videos. The videos and a very short corresponding assignment together form a PreLab and are made available to students to access at appropriate times in the course.
Some PreLabs were created to expose the students to statistical software details. Other PreLabs incorporate an available online learning resource or applet which allows students to gain a deeper understanding of a course concept through simulation and visualization. Not all on-line learning resources are ready to use 'as in' in a course. Some may be lacking a preface or description on how they are to be used; others may use slightly different notation or language than your students are accustomed to; a few may even contain an error or item that needs some clarification. One solution to such difficulties was to create a video wrapper so students can see how the applet works while receiving guidance from the instructor.
In this webinar we will share the success story of how one introductory Statistics course integrated these video wrappers into the course and the discuss other possible applications.
Kirk Anderson, Grand Valley State University
Tuesday, August 11, 2009 - 2:00pm
Many of us, while teaching an introductory statistics course, have mentioned some of the history behind the methodology, perhaps just in passing. We might remark that an English chap by the name of R. A. Fisher is responsible for a great deal of the course content. We could further point out that the statistical techniques used in research today were developed within the last century, for the most part. At most, we might reveal the identity of the mysterious "Student" when introducing the t-test to our class. I propose that we do more of this. This webinar will highlight some opportunities to give brief history lessons while teaching an introductory statistics course.
Margo Vreeburg Izzo, The Ohio State University Nisonger Center
Tuesday, July 14, 2009 - 2:00pm
Teaching a diverse college population is a challenge that most college faculty face each day. Universal Design for Learning is an approach to teaching that takes into consideration different student experiences, different cultures, and other issues such as disability. By examining curriculum and instruction through the context of universal design, you can engage as many students as possible in your college classroom and increase achievement by engaging students through a variety of methods ranging from electronic voting machines during class lectures to podcasts to deliver/reinforce essential course content.
Dalene Stangl, Duke University
Tuesday, June 9, 2009 - 2:00pm
This webinar will present the core materials I use to teach Bayesian inference in undergraduate service courses geared toward social science, natural science, pre-med, and/or pre-law students. During the semester this material is presented after completing all chapters of the book Statistics by Freedman, Pisani, and Purves. It uses math at the level of basic algebra.
Laura Kubatko, The Ohio State University; Danny Kaplan, Macalester College; and Jeff Knisley, East Tennessee State University
Tuesday, May 12, 2009 - 2:00pm
National reports such as Bio2010 have called for drastic improvements in the quantitative education that biology students receive. The three panelists are involved in three differently structured integrative programs aimed to give biology students the statistics that are useful in learning and doing biology.
The three programs have some surprising things in common for teaching introductory statistics. All three involve connecting calculus and statistics. All three reach beyond the mathematical topics usually encountered in intro statistics in important ways. All three aim to keep the mathematics and statistics strongly connected to biology.
The panelists will describe their different approaches to teaching statistics for biology and discuss how and why an integrated approach gives advantages. Important issues are how to tie statistics advantageously with calculus, how to keep "advanced" mathematical and statistical topics accessible to introductory-level biology students, and how to employ computation productively. The discussion will contrast a comprehensive "team" approach (at ETSU) with stand-alone courses (at Macalester and at OSU) and will refer to the institutional opportunities and constraints that have shaped the programs at their different institutions.
Allan Rossman & Beth Chance, Cal Poly - San Luis Obispo; and John Holcomb, Cleveland State University
Tuesday, April 14, 2009 - 2:00pm
We present ideas and activities for helping students to learn fundamental concepts of statistical inference with a randomization-based curriculum rather than normal-based inference. We propose that this approach leads to deeper conceptual understanding, makes a clear connection between study design and scope of conclusions, and provides a powerful and generalizable analysis framework. During this webinar we present arguments in favor of such a curriculum, demonstrate some activities through which students can investigate these concepts, highlight some difficulties with implementing this approach, and discuss ideas for assessing student understanding of inference concepts and randomization procedures.
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Jennifer Kaplan, Michigan State University
Tuesday, March 10, 2009 - 2:00pm
Central to the recommendations for teaching introductory statistics made by the GAISE committee were the following: foster active learning in the classroom, use assessment to improve and evaluate student learning, and use real data (GAISE, 2006). This session will illustrate how personal response systems (clickers) can be used to address the realization of these three recommendations in large lecture classes (over 70 students). The session will discuss general issues of the implementation of clickers and then provide an example of each of the following three uses of clickers in the classroom: 1) questions designed to highlight common conceptual misunderstandings in statistics, 2) questions designed as review questions for topics already addressed, and 3) questions that were part of a class activity to help students learn a concept.
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Andrew Zieffler, Bob delMas, and Joan Garfield, University of Minnesota
Tuesday, February 10, 2009 - 2:00pm
This webinar presents an overview of the materials and research-based pedagogical approach to helping students reason about important statistical concepts. The materials presented were developed by the NSF-funded AIMS (adapting and Implementing Innovative Materials in Statistics) project at the University of Minnesota (www.tc.umn.edu/~aims).
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