Lawrence M. Lesser and Amy E. Wagler, The University of Texas at El Paso
Wednesday, March 18, 2015 - 12:30pm
We motivate and illustrate a lesser-known dynamic physical model for the median, offer pedagogical discussion and support, and share results of a pilot assessment with pre-service middle school teachers.
Before the webinar, we invite you to browse our article "http://www.amstat.org/publications/jse/v22n3/lesser.pdf" , or at least watch the 1-minute video http://www.amstat.org/publications/jse/v22n3/pulley_loop_physical_model_of_median.html of the model in action.
Kendra K. Schmid and Erin Blankenship, University of Nebraska
Tuesday, February 17, 2015 - 2:00pm
This presentation discusses the creation and delivery of an introductory statistics course as part of a master’s degree program for in-service mathematics teachers. We give an overview of the master’s degree program and discuss aspects of the course, including the goals for the course, course planning and development, the instructional team, the evolution of the course over multiple iterations. In addition, we present lessons learned through five offerings including what we have learned about its value to the middle-level teachers who have participated.
Shaun S. Wulff, University of Wyoming
Tuesday, November 18, 2014 - 3:00pm
Students need exposure to Bayesian thinking at early stages in their mathematics and statistics education. While many students in upper level probability courses can generally recite the differences in the Frequentist and Bayesian inferential paradigms, these students often struggle using Bayesian methods when conducting data analysis. Specifically, students tend to struggle translating subjective belief to the specification of a prior distribution and the incorporation of uncertainty in the Bayesian inferential approach. The purpose of this webinar is to present a hands-on activity involving the Beta-Binomial model to facilitate an intuitive understanding of the Bayesian approach through subjective problem formulation which lies at the heart of Bayesian statistics.
Stanley A. Taylor & Amy E. Mickel; California State University, Sacramento
Saturday, October 18, 2014 - 3:00pm
We present a data set and case study exercise that can be used by educators to teach a range of statistical concepts including Simpson’s paradox. The data set and case study are based on a real-life scenario where there was a claim of discrimination based on ethnicity. The exercise highlights the importance of performing rigorous statistical analysis and how data interpretations can accurately inform or misguide decision makers.
Eiki Satake, Emerson College
Saturday, October 18, 2014 - 3:00pm
Eiki's presentation begins at the 28 minute mark. See Part 1.
Jennifer Kaplan, The University of Georgia
Tuesday, September 16, 2014 - 12:00pm
Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. They are included in introductory statistics texts, research methods texts, and in the popular press, yet students often have difficulty interpreting the information conveyed by a histogram. This talk will identify and discusses four misconceptions prevalent in student understanding of histograms. In addition, pre- and post-test results on an instrument designed to measure the extent to which the misconceptions persist after instruction will be presented. The results indicate not only that some of the misconceptions are commonly held by students prior to instruction, but also that they persist after instruction. Future directions for teaching and research are considered.
Caroline Brophy, National University of Ireland Maynooth
Tuesday, June 17, 2014 - 12:00pm
Active learning opportunities can be difficult to generate when teaching large groups of students. In this webinar, I will present an experiment using Sudoku puzzles that can be easily conducted in a lecture with 300 (or more) students. The factor manipulated in the experiment is the type of Sudoku puzzle and there are four types, which are each the same puzzle but with different characters. The experiment yields a rich data set which can be used to illustrate basic statistical methods such as chi-square test for independence of categorical variables, through to more complicated analyses such as survival analysis techniques. I will outline the experiment and give an overview of the teaching opportunities that the data present.
Amy G. Froelich, Iowa State University
Tuesday, April 15, 2014 - 12:00pm
As a part of an opening course survey, data on eye color and gender were collected from students enrolled in an introductory statistics course at a large university over a recent four year period. Biologically, eye color and gender are independent traits. However, in the data collected from our students, there is a statistically significant dependence between the two variables. In this article, we present two ideas for using this data set in the classroom, and explore the potential reasons for the dependence between the two variables in the population of our students.
Alison Gibbs & Emery Goossens, University of Toronto
Tuesday, March 18, 2014 - 12:00pm
Our current students are among the first to have been vaccinated against HPV. Have they ever considered how the accumulation of the evidence for the efficacy of the vaccine resulted in recommendations for its widespread provision? Using data sourced from a meta-analysis of clinical trials for HPV vaccines, we will examine this evidence. We will show how these data can be used to illustrate applications of methods of categorical data analysis, for students in courses at a variety of levels. And we will describe how this case study can be used to promote discussion of concepts in the design of experiments, statistical concerns such as independence of observations, and the importance of context in the interpretation of the results of data analyses.
Brad Bailey & Dianna Spence, University of North Georgia
Tuesday, February 18, 2014 - 12:00pm
In this presentation we will discuss student-directed discovery projects in statistics, which are intended as the means through which statistical content is taught to the students. In particular, we will delineate the purpose and scope of a project covering linear regression analysis and another project covering comparisons with basic t-tests. We will describe curriculum materials developed to help instructors facilitate such projects and provide the web address where these materials can be accessed. We will give examples of how instructors use the curriculum materials to guide students through the projects' stages. In particular, the materials can be used to provide the students with clear information about the project requirements and what activities the students are expected to be engaged in during each phase of the student-directed projects. These projects are truly student-directed in that the students select their own research topic, define their own variables, and devise and carry out their own data collection plan before analyzing and interpreting their data. The students report their results in two forms: a written report is provided to the instructor and a brief formal presentation is made before the rest of the class. In both formats, the students report the findings of their research project, as well as explain why they chose the particular research topic, explain how they gathered, organized and analyzed the data and list any short-comings they perceive in their own project. Our presentation will include specific examples of projects that students have conducted. Finally, we will also discuss the rationale for assigning such projects, including the potential benefits of such projects - benefits suggested by both prior and on-going research - and possible factors mediating those benefits.