Resource Library

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.

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.

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.

April 10, 2007 webinar presented by Maria C. Pruchnicki, The Ohio State University, and hosted by Jackie Miller, The Ohio State University. Distance education and online learning opportunities, collectively known as "e-learning", are becoming increasingly used in higher education. Nationally, online enrollment increased to 3.2 million students in 2005, compared to 2.3 million in 2004. Furthermore, nearly 60% of higher education institutions identify e-learning as part of their long-term education strategy. Newer educational technologies including course management systems and Internet-based conferencing software can be used to both deliver content and engage participants as part of a social learning community. However, even experienced faculty can face pedagogical and operational challenges as they transition to the online environment. This interactive presentation discusses a systematic approach to developing web-based instruction, with an Ohio State University experience as a case example.

The International Statistical Literacy Project (ISLP) puts out a newsletter bimonthly. According to ISLP, "The mission of the International Statistical Literacy Project (ISLP) is to support, create and participate in statistical literacy activities and promotion around the world." This newsletter is a way to get information out to those interested.

This limerick was written by Columbia University professor of biostatistics, Joseph L. Fleiss (1938 -2003). It was published along with three other limericks by Dr. Fleiss in a letter to the editor of "The American Statistician" (volume 2; 1967, page 49). It was written while he worked as a biostatistician at the Department of Mental Hygiene of the State of New York just prior to receiving his Ph.D. and joining the faculty at Columbia.

This in-class demonstration combines real world data collection with the use of the applet to enhance the understanding of sampling distribution. Students will work in groups to determine the average date of their 30 coins. In turn, they will report their mean to the instructor, who will record these. The instructor can then create a histogram based on their sample means and explain that they have created a sampling distribution. Afterwards, the applet can be used to demonstrate properties of the sampling distribution. The idea here is that students will remember what they physically did to create the histogram and, therefore, have a better understanding of sampling distributions.

In this hands-on activity, students count the number of chips in cookies in order to carry out an independent samples t-test to see if Chips AhoyŒ¬ cookies have a higher, lower, or different mean number of chips per cookie than a supermarket brand. First there is a class discussion that can include concepts about random samples, independence of samples, recently covered tests, comparing two parameters with null and alternative hypotheses, what it means to be a chip in a cookie, how to break up the cookies to count chips, and of course a class consensus on the hypotheses to be tested. Second the students count the number of chips in a one cookie from each brand, and report their observations to the instructor. Third, the instructor develops the independent sample t-test statistic. Fourth, the students carry out (individually or as a class) the hypothesis test, checking the assumptions on sample-size/population-shape.