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.
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.
Eiki Satake, Emerson College
Saturday, October 18, 2014 - 3:00pm
Eiki's presentation begins at the 28 minute mark. See Part 1.
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.