Lecture Examples

  • August 14, 2007 Teaching & Learning webinar presented by Oded Meyer, Carnegie Mellon University, and hosted by Jackie Miller, The Ohio State University. Carnegie Mellon University was funded to develop a "stand-alone" web-based introductory statistics course, and for several semesters they studied different ways in which the course could be used to support instruction. In this presentation, Dr. Meyer discusses some of the challenges in developing such a learning environment and ways in which the course tries to address them, as well as describing the design and results of accompanying studies.

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  • November 13, 2007 Teaching and Learning webinar presented by Michael Rodriguez and Andrew Zieffler, University of Minnesota, ad hosted by Jackie Miller, The Ohio State University. This webinar includes an introduction to the idea of assessment for learning - assessments that support learning, enhance learning, and provides additional learning opportunities that support instruction. Several fundamental measurement tools are described to support the development of effective assessments that work.
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  • December 11, 2007 Teaching and Learning webinar presented by Mark L. Berenson, Montclair State University, and hosted by Jackie Miller, he Ohio State University. As we consider how we might improve our introductory statistics courses, we are constrained by a variety of environmental/logistical and pedagogical issues that must be addressed if we want our students to complete the course saying it was useful, it was relevant and practical, and that it increased their communicational, computational, technological and analytical skills. If not properly considered, such issues may result in the course being considered unsatisfying, incomprehensible, and/or unnecessarily obtuse. This Webinar focuses on key course content concerns that must be addressed and engages participants in discussing resolutions. Participants also had the opportunity to describe and discuss other content barriers to effective statistical pedagogy.

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  • 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.
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  • 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.
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  • April 8, 2008 Teaching and Learning webinar presented by Beth Chance and Allan Rossman, Cal Poly - San Luis Obispo and hosted by Jackie Miller, The Ohio State University. 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. This webinar describes and discusses a data-centered course that we have developed for mathematically inclined undergraduates.

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  • 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.
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  • 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.
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  • 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.
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  • 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.
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