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.
Herbert Lee, University of California - Santa Cruz
Tuesday, April 28, 2009 - 2:30pm
Getting and retaining the attention of students in an introductory statistics course can be a challenge, and poor motivation or outright fear of mathematical concepts can hinder learning. By using an example as familiar and comforting as chocolate chip cookies, the instructor can make a variety of statistical concepts come to life for the students, greatly enhancing learning. Topics from variability and exploratory data analysis to hypothesis testing and Bayesian statistics can be illuminated with cookies.
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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Nicholas Horton, Smith College
Tuesday, March 24, 2009 - 2:30pm
Students have a hard time making the connection between variance and risk. To convey the connection, Foster and Stine (Being Warren Buffett: A Classroom Simulation of Risk and Wealth when Investing in the Stock Market (see materials) The American Statistician, 2006, 60:53-60) developed a classroom simulation. In the simulation, groups of students roll three colored dice that determine the success of three "investments". The simulated investments behave quite differently. The value of one remains almost constant, another drifts slowly upward, and the third climbs to extremes or plummets. As the simulation proceeds, some groups have great success with this last investment--they become the "Warren Buffetts" of the class. For most groups, however, this last investment leads to ruin because of variance in its returns. The marked difference in outcomes shows students how hard it is to separate luck from skill. The simulation also demonstrates how portfolios, weighted combinations of investments, reduce the variance. In the simulation, a mixture of two poor investments is surprisingly good.
In this webinar, the activity will be demonstrated along with a discussion of goals, context, background materials, class handouts, and references.
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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Jo Hardin, Pomona College
Tuesday, January 13, 2009 - 2:00pm
This webinar will discuss the development and teaching of a freshman seminar course. In this course, students investigate the practical, ethical, and philosophical issues raised by the use of statistics and probabilistic thinking in realms such as politics, medicine, sports, the law, and genetics. Students explore issues from fiction, the mainstream media, and scientific articles in peer-reviewed journals. To do all of this, they must consider a wide range of statistical topics as well as encountering a range of uses and abuses of statistics in the world today.
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John H. Walker, California Polytechnic State University
Tuesday, December 9, 2008 - 2:00pm
Ethics play an important role in statistical practice. How can we educate our students about statistical ethics--especially when our courses are already packed with so much...statistics? At the Joint Statistical Meetings in August, I was the discussant in a session on "Teaching Ethics in Statistics Class." First, I will briefly review the points raised by the speakers in that session. George McCabe (Purdue) contrasted the "old" introductory statistics course with its emphasis on methodology to the "new" course. Patricia Humphrey (Georgia Southern) spoke about how she covers ethical data collection in her introductory classes. Paul Velleman (Cornell) talked about the role of judgment in statistical model building and how it makes students (and sometimes us) uncomfortable. I will discuss each of these points in the context of the American Statistical Association's "Ethical Guidelines for Statistical Practice" as well as my own experiences in teaching statistical ethics in an undergraduate capstone course for statistics majors. I will close with an example of statistical ethics in the use of multiple comparison procedures.
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Xiao-Li Meng, with Happy Team members: Yves Chretien, Paul Edlefsen, Kari Lock, and Cassandra Wolos; Department of Statistics, Harvard University
Tuesday, November 18, 2008 - 2:00pm
Statistics 105 is a team-designed course that has received local media attention (e.g., www.news.harvard.edu/gazette/2008/02.14/11-stats.html). Its course description promises the following:
Discover an appreciation of statistical principles and reasoning via "Real-Life Modules" that can make you rich or poor (financial investments), loved or lonely (on-line dating), healthy or ill (clinical trials), satisfied or frustrated (chocolate/wine tasting) and more. Guaranteed to bring happiness (or misery) both to students who have never taken a previous statistics course, and to those who have taken statistics and want to see how statistical thinking applies to so many areas of life.
This webinar will reveal its history, pedagogical motivation, innovations, and challenges along the way.
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Daniel Kaplan, Macalester College
Tuesday, October 14, 2008 - 2:00pm
George Cobb describes the core logic of statistical inference in terms of the three Rs: Randomize, Repeat, Reject. (See repositories.cdlib.org/uclastat/cts/tise/vol1/iss1/art1) Note that all three Rs involve process or action. Teaching this core logic is more effective when students are able to carry out these actions on real data.
In this webinar, I'll show how to use computers effectively with introductory-level students to teach them the three Rs of inference. To do this, I will use a another R: the statistical software package.
The simulations that will be carried out involve constructing confidence intervals, demonstrating the idea of "coverage," hypothesis testing, and confounding and covariation.
Although R is professional-level software, it's very easy to use in an introductory setting, as I have been doing for the last decade. The key is to use flexible and concise operators. I'll provide these to the seminar participants.
To follow the seminar successfully, you do NOT need to know anything about R or computer programming. However, you should install R on a computer so that you can follow along. Instructions for doing this, and a short introduction to simple R commands, are available at www.macalester.edu/~kaplan/ISM/draft-intro.pdf (PDF) (see Section 1.4).
A note from the presenter:
Dear Webinar Participants,
Here are the slides for next Tuesday's webinar. I'm sending them out in advance because they contain information on how to install R and the datasets, etc. for the webinar. The slides also contain some background and extension material that there won't be time to go over during the webinar --- these are the slides marked with a dark band at the bottom.
I'm also attaching a "crib sheet." Although there are just a few commands that you will need to learn to carry out the simulations described in the webinar, it's convenient to have these all listed on one sheet.
This is the first time I have given a presentation using web-based software. I have been practicing a bit. One of the things I have realized is how different the webinar format is from the conventional face-to-face situation. In classes and workshops, I have always liked to have students or participants use the computer at the same time as we are talking about the statistical principles and how the computer supports them. Inevitably, people make mistakes, but these become learning experiences since I am there to help get them quickly back on track.
In the webinar format, however, I have no practical way to see what you are typing in your own R sessions and no way to respond quickly to errors. So, I'm concerned that people who are trying to follow along in their own R session will just get distracted. I suggest that the best way to proceed, if you do get distracted by a small error, is to stop and follow the webinar --- I hate to say it --- "passively." Then, after the webinar, we can sort through any problems in a one-on-one format. I find this regrettable, since I think people learn better when they are actively engaged with the material, and because the basic premise of this webinar is that when students actively implement the logic of statistical inference, they come to a faster and better understanding of it.
Regards,
Danny Kaplan
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