• Designing Opportunities to Learn to Teach Statistics: Lessons from a MOOC for Educators

    Hollylynne Lee, NC State University, Friday Institute for Educational Innovation
    Tuesday, August 16, 2016 - 2:00pm
    Professional development for educators can be done in a flexible format that meets the needs of teachers of statistics in a variety of contexts. Design principles and sample learning opportunities will be shared that are part of the Teaching Statistics Through Data Investigations MOOC for Educators. The course is offered several times a year and thus far has served over 2500 educators from all 50 states, and over 45 countries. See http://friday.institute/tsdi.
  • Using Media Clips to Introduce Topics in Statistics

    James Bush, Waynesburg University, Waynesburg, PA
    Tuesday, July 12, 2016 - 2:00pm
    This webinar will present several media clips from popular films and television programs and show how they can be used to introduce topics in a first-year statistics course. A simulation-based activity motivated by one of the clips will be demonstrated.
  • Data Exploration with CODAP

    William Finzer, Concord Consortium
    Tuesday, June 14, 2016 - 2:00pm
    The Common Online Data Analysis Platform (CODAP) is an online, free, and open source descendant of Fathom and TinkerPlots (though still far from a replacement for them). We’ll look at ways you can already use CODAP in the classroom and understand where ongoing development at Concord Consortium will take it.
  • Reflections on making the switch to a simulation-based inference curriculum

    Julie Clark (Hollins University), Lacey Echols (Butler University), Dave Klanderman (Trinity Christian College) and Laura Schultz (Rowan University), moderated by Nathan Tintle, Dordt College
    Tuesday, September 8, 2015 - 12:00pm
    In this webinar some recent new adopters of simulation-based inference (SBI) curricula will share their responses to questions such as: What made you switch to SBI from a traditional curriculum? What have you enjoyed most about the switch? What were some of the challenges in switching? What would you do different next time?
  • Updating the Guidelines for Assessment and Instruction in Statistics Education (GAISE)

    Michelle Everson, The Ohio State University and Megan Mocko, University of Florida
    Tuesday, July 7, 2015 - 12:00pm
    In 2005, the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report was endorsed by the American Statistical Association (ASA). Although the original six recommendations put forward in this report have stood the test of time, we now live in an increasingly data-centric world where our students have access to technologies that were not in existence in 2005. The ASA has therefore made it a priority to revise GAISE so that it continues to be easily and clearly applicable to modern-day teachers of introductory statistics courses. To accomplish this goal, a committee was formed and charged with the task of updating this landmark report. Two members of this committee will facilitate this webinar. In the webinar, we will reflect on how the landscape has changed in Statistics Education over the past 10 years, and we will discuss the process of updating and revising the GAISE report. The audience will have the opportunity to provide feedback and share ideas about the proposed revisions.
  • ASA DataFest: Teaching Data Science through Data Hackathons

    Rob Gould, UCLA
    Tuesday, May 19, 2015 - 1:30pm
    We'll describe and explain ASA DataFest, a Big Data Hackathon for undergraduate students, and offer advice on how to throw your own.
  • Examining Student Conceptions of Covariation: A Focus on the Line of Best Fit

    Stephanie Casey, Eastern Michigan University
    Tuesday, May 12, 2015 - 12:00pm
    This webinar will present research regarding students' conceptions of the line of best fit prior to formal instruction on the topic. Task-based interviews were conducted with thirty-three eighth grade students, focused on tasks that asked them to place the line of best fit on a scatterplot and explain their reasoning as they did so. Results regarding descriptions and categorizations of students' meanings of the line of best fit and criteria they use when placing it will be presented, including video excerpts of the student interviews. Implications for the teaching and learning of the line of best fit will be discussed.
  • Developing K-12 Teachers' Understanding of Statistics

    Tim Jacobbe, University of Florida
    Saturday, April 18, 2015 - 2:00pm
    Expectations for teaching statistics have been increased without adequately addressing teachers' preparation. This session will share results from teachers' performance on the NSF-funded LOCUS assessments as well as identify resources that may be used in training teachers during preparation and professional development programs.
  • 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.