Webinars

  • Statistics and Society: Updating the curriculum of an introductory statistical literacy course for the modern student

    Ellen Gundlach, Purdue University
    Tuesday, March 10, 2015 - 2:00pm
    Strategies for including important (and sometimes controversial), modern issues from society into an introductory statistical literacy course for liberal arts students will be discussed, including several projects which have been successfully used for 500 students split between large-lecture traditional, fully online, and flipped sections. Topics include advertisement analysis, big data, ethics, social media article discussions, and a service learning project. These new topics and projects capture student interest and show them how relevant statistical literacy is to their daily lives.
  • Teaching precursors to data science in introductory and second courses in statistics

    Nicholas J. Horton, Professor of Statistics, Amherst College
    Tuesday, February 24, 2015 - 2:00pm
    Statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world. Data science is an important part of modern statistics, but our introductory and second statistics courses often neglect this fact. This webinar discusses ways to provide a practical foundation for students to learn to “compute with data” as defined by Nolan and Temple Lang (2010), as well as develop “data habits of mind” (Finzer, 2013). We describe how introductory and second courses can integrate two key precursors to data science: the use of reproducible analysis tools and access to large databases. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically in the era of big data.
  • The Development and Evolution of an Introductory Statistics Course for In-Service Middle-Level Mathematics Teachers

    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.
  • What is the probability you are a Bayesian?

    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.
  • Simpson's Paradox: A Data Set and Discrimination Case Study Exercise

    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.
  • Teaching an Application of Bayes' Rule for Legal Decision-Making: Measuring the Strength of Evidence

    Eiki Satake, Emerson College
    Saturday, October 18, 2014 - 3:00pm
    Eiki's presentation begins at the 28 minute mark. See Part 1.
  • The Wikipedia Makeover: Spreading Stat Ed's Joy and Wisdom

    Ethan Brown, University of Minnesota
    Tuesday, September 23, 2014 - 1:00pm
    Wikipedia's page on Statistics Education gets hundreds of hits every week, but until recently the page gave a very limited impression of our discipline. A group at the University of Minnesota has been regularly meeting since fall 2012 to research, update, and improve the Wikipedia coverage of statistics education. We have only begun to scratch the surface of Wikipedia's power to collect and widely disseminate the what, when, who, where, and why of teaching and learning statistics. Come hear about what we've done so far, and how you can get involved in spreading the word about the resources available to statistics educators worldwide.
  • Everyone Can Read a Histogram, or can they?

    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.
  • Engaging Students in a Large Lecture: An Experiment using Sudoku Puzzles

    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.
  • Sampling Variability: A hot topic in the Common Core

    Anna Bargagliotti (for the Project-SET team), Loyola Marymount University
    Tuesday, June 10, 2014 - 12:00pm
    The Common Core State Standards (CCSS) include much more statistics content than previous standards. Their adoption has created the opportunity and necessity for nearly all middle school and high school mathematics teachers to be prepared to teach a substantial amount of statistics. This session will focus on the topic of sampling variability, a topic that is greatly emphasized in the middle and high school grades in the CCSS. We will present a research-based learning trajectory to help guide teacher preparation on this topic. In addition, we will discuss several unexpected misconceptions that emerged while testing the trajectory with high school teachers. As a group, we will work through an activity together to illustrate how to use the trajectory with teachers.

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