Lecture Examples

  • 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.
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  • This song is an ode to bad teaching in statistics written by Dennis Pearl to be sung to the tune of Roger Miller's 1965 classic country/pop tune "King of the Road." Musical accompaniment realization and vocals are by Joshua Lintz from University of Texas at El Paso.
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  • July 10, 2007 Teaching & Learning Webinar presented by Larry Lesser, University of Texas at El Paso, and hosted by Jackie Miler, The Ohio State University. Drawing from (and expanding upon) his article in the March 2007 Journal of Statistics Education, Larry Lesser discusses and invite discussion about examples, resources and pedagogy associated with this meaningful way of engaging students in the statistics classroom.
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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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  • 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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