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?
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.
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.
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.
Tim Jacobbe, University of Florida
Tuesday, April 14, 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.
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.
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.
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.
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.
Aimee Schwab & Erin Blankenship; University of Nebraska, Lincoln
Tuesday, March 11, 2014 - 4:00pm
Like many other research universities, the University of Nebraska-Lincoln relies on graduate student instructors to cover a large portion of the instructional load in the introductory course. In order to better prepare new graduate student instructors, we have implemented a mentoring program that pairs new GTAs with experienced graduate student instructors. Through the mentoring program, the new GTA has a semester to acclimate to graduate school and their new role as instructor, and the senior GTA has the opportunity to emerge as a teacher leader.