Resource Library

April 14, 2009 Teaching and Learning webinar presented by Beth Chance and Allan Rossman, Cal Poly, and John Holcomb, Cleveland State University, and hosted by Jackie Miller, The Ohio State University. This webinar presents ideas and activities for helping students to learn fundamental concepts of statistical inference with a randomization-based curriculum rather than normal-based inference. The webinar proposes 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 arguments are presented in favor of such a curriculum, demonstrate some activities through which students can investigate these concepts, highlights some difficulties with implementing this approach, and discusses ideas for assessing student understanding of inference concepts and randomization procedures.

webinar illustrates 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 discusses general issues of the implementation of clickers and then provides 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.

January 13, 2009 Teaching and Learning webinar presented by Jo Hardin, Pomona College and hosted by Jackie Miller, The Ohio State University. This webinar discusses 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.

December 9, 2008 Teaching and Learning webinar presented by John H. Walker, California Polytechnic State University and hosted by Jackie Miller, The Ohio State University. 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, 2008 Dr. Walker was the discussant in a session on "Teaching Ethics in Statistics Class." The webinar first briefly reviews 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. The webinar presentation discusses each of these points in the context of the American Statistical Association's "Ethical Guidelines for Statistical Practice" as well as discussing experiences in teaching statistical ethics in an undergraduate capstone course for statistics majors. It closes with an example of statistical ethics in the use of multiple comparison procedures.

November 18, 2008 Teaching and Learning webinar presented by Xiao-Li Meng, Harvard University and hosted by Jackie Miller, The Ohio State University. 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 reveals its history, pedagogical motivation, innovations, and challenges along the way

October 14, 2008 Teaching and Learning webinar presented by Daniel Kaplan, Macalester College and hosted by Jackie Miller, The Ohio State University. George Cobb describes the core logic of statistical inference in terms of the three Rs: Randomize, Repeat, Reject. 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. This webinar shows how to use computers effectively with introductory-level students to teach them the three Rs of inference. This is done with another R: the statistical software package. The simulations that are carried out involve constructing confidence intervals, demonstrating the idea of "coverage," hypothesis testing, and confounding and covariation.

August 12, 2008 Teaching and Learning webinar presented by Kathryn Plank, The Ohio State University; and Michele DiPietro, Carnegie Mellon University and hosted by Jackie Miller, The Ohio State University. 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. This webinar explores 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.

June 10, 2008 Teaching and Learning webinar presented by Robert delMas, University of Minnesota and Marsha Lovett, Carnegie Mellon University and hosted by Jackie Miller, The Ohio State University. There is a large body of research on the mechanisms underlying student learning. This webinar explores 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 is given to show how these principles can be integrated to develop and support our students' conceptual understanding.

February 12, 2008 Teaching and Learning webinar presented by Christopher J. Malone, Winona State University and hosted by Jackie Miller, The Ohio State University. 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. In this webinar, Dr. Malone proposes a new sequencing of topics that may enhance students' ability to perform a complete statistical investigation from beginning to end.

January 8, 2008 Teaching and Learning webinar presented by Dennis Pearl, The Ohio State University and hosted by Jackie Miller, The Ohio State University. This presentation describes the "Buffet" method for teaching multi-section courses. In this method, students are offered a choice of content delivery strategies designed to match different individual learning styles. The choice is exercised through an on-line "contract" entered into by students at the beginning of the term. The webinar describes the Ohio State experiences with the buffet strategy and discusses how key elements of the strategy can also be adapted to smaller classes to improve student learning.