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 ET
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.
Watch Webinar Recording (FLASH)
Daniel Kaplan, Macalester College
Tuesday, October 14, 2008 - 2:00pm ET
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.
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Joan Garfield & Michelle Everson, University of Minnesota
Tuesday, September 9, 2008 - 2:00pm ET
This webinar discusses issues and challenges in preparing teachers of statistics at the secondary and college level. We then provide a case study of a graduate level course taught at the University of Minnesota that focuses on developing excellent teachers of statistics. The course is based on the GAISE guidelines and helps the students develop both knowledge of teaching (pedagogical knowledge) and specific knowledge about teaching statistics (pedagogical content knowledge). Topics, readings, activities, assessments, and discussions are described. In addition, we discuss how the course was transformed from a face-to-face setting to an online environment.Watch Webinar Recording (FLASH)
Kathryn Plank, The Ohio State University; and Michele DiPietro, Carnegie Mellon University
Tuesday, August 12, 2008 - 2:00pm ET
There are many good reasons to incorporate thinking about diversity into a course, not the least of which is that it can have a real impact on student learning and cognitive development. In this webinar, we will explore both how the tools of statistics can help students better understand complex and controversial issues, and, in the other direction, how using these complex and controversial issues can help facilitate deeper learning of statistics.Watch Webinar Recording (FLASH)
Shonda Kuiper, Grinnell College
Tuesday, July 8, 2008 - 2:00pm ET
Many instructors use projects to ensure that students experience the challenge of synthesizing key elements learned throughout a course. However, students can often have difficulty adjusting from traditional homework to a true research project that requires searching the literature, transitioning from a research question to a statistical model, preparing a proposal for analysis, collecting data, determine an appropriate technique for analysis, and presenting the results. This webinar presents multi-day lab modules that bridge the gap between smaller, focused textbook problems to large projects that help students experience the role of a research scientist. These labs can be combined to form a second statistics course, individually incorporated into an introductory statistics course, used to form the basis of an individual research project, or used to help students and researchers in other disciplines better understand how statisticians approach data analysis.Watch Webinar Recording (FLASH)
Bob delMas, University of Minnesota; and Marsha Lovett, Eberly Center for Teaching Excellence, Carnegie Mellon University
Tuesday, June 10, 2008 - 3:00pm ET
There is a large body of research on the mechanisms underlying student learning. In this webinar, we will explore four principles distilled from this research - the role of prior knowledge, how students organize knowledge, meaningful engagement, and goal-directed practice and feedback - and illustrate their application in the teaching of statistics. A more detailed example will be given to show how these principles can be integrated to develop and support our students' conceptual understanding.Watch Webinar Recording (FLASH)
Joy Jordan, Lawrence University
Tuesday, May 13, 2008 - 2:00pm ET
Writing can be an effective instrument for students learning new concepts, and there is a plethora of writing-to-learn research. This Webinar will summarize important findings from the writing literature, as well as provide specific writing-assignment examples for the introductory statistics classroom.Watch Webinar Recording (FLASH)
Beth Chance & Allan Rossman, Cal Poly - San Luis Obispo
Tuesday, April 8, 2008 - 2:00pm ET
Math majors, and other mathematically inclined students, have typically been introduced to statistics through courses in probability and mathematical statistics. We worry that such a course sequence presents mathematical aspects of statistics without emphasizing applications and the larger reasoning process of statistical investigations. In this webinar we describe and discuss a data-centered course that we have developed for mathematically inclined undergraduates.Watch Webinar Recording (FLASH)
Deborah Nolan, University of California at Berkeley
Tuesday, March 11, 2008 - 1:00pm ET
Computing is an increasingly important element of statistical practice and research. It is an essential tool in our daily work, it shapes the way we think about statistics, and broadens our concept of statistical science. Although many agree that there should be more computing in the statistics curriculum and that statistics students need to be more computationally capable and literate, it can be difficult to determine how the curriculum should change because computing has many dimensions. In this webinar we explore alternatives to teaching statistics that include innovations in data technologies, modern statistical methods, and a variety of computing skills that will enable our students to become active and engaged participants in scientific discovery.Watch Webinar Recording (FLASH)
Christopher J. Malone, Winona State University
Tuesday, February 12, 2008 - 2:00pm ET
The procedural steps involved in completing a statistical investigation are often discussed in an introductory statistics course. For example, students usually gain knowledge about developing an appropriate research question, performing appropriate descriptive and graphical summaries, completing the necessary inferential procedures, and communicating the results of such an analysis. The traditional sequencing of topics in an introductory course places statistical inference near the end. As a result, students have limited opportunities to perform a complete statistical investigation. We propose a new sequencing of topics that may enhance students' ability to perform a complete statistical investigation from beginning to end.Watch Webinar Recording (FLASH)