Ellen Gundlach, Purdue University
Wednesday, August 19, 2015 - 12:00pm
In this presentation, we will compare three delivery methods of an introductory statistical literacy course, all taught by the same instructor in the same semester for over 400 students. The complications of defining specific delivery methods and the pros and cons of choices of assessments will also be discussed.
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.
Emily Casleton and Ulrike Genschel, Iowa State University
Tuesday, April 21, 2015 - 1:00pm
In this webinar, we will present lecture material and activities that introduce metrology, the science of measurement, which were developed and tested in a pilot study at Iowa State University. Our motivation for the newly developed material stems from the observation that many undergraduate students who have just completed an introductory statistics course still lack a deeper understanding of variability and enthusiasm for the field of statistics. The materials explain how to characterize sources of variability in a dataset, in a way that is natural and accessible, because the sources of variability are observable. Everyday examples of measurements, such as the amount of gasoline pumped into a car, are presented, and the consequences of variability within those measurements are discussed. A corresponding article in the November issue of Journal of Statistics Education shows most students who were exposed to the material improved their understanding of variability and had a greater appreciation of the value 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.
André Michelle Lubecke, Lander University
Tuesday, March 24, 2015 - 2:00pm
A few inexpensive items have ‘inspired’ a number of classroom experiences that have students discussing experimental design issues and/or generating data in relatively fast and fun ways. This webinar will present a few activities that are often cited as favorites by students taking a statistics course as part of their General Education curriculum. Some possible extensions/variations that could be used in other types of courses will also be discussed. These activities use only an inexpensive set of wooden farm animal puzzles, dice, cards, and a stopwatch.
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.
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.