Amy Nowacki, Cleveland Clinic and Cleveland Clinic Lerner College of Medicine
Wednesday, November 18, 2015 - 12:00pm
Statistics courses that focus on data analysis in isolation, discounting the scientific inquiry process, may not motivate students to learn the subject. By involving students in other steps of the inquiry process, such as generating hypotheses and data, students may become more interested and vested in the analysis step. Additionally, such an approach might better prepare students to tackle real research questions outside of the statistics classroom. Presented here is a classroom activity utilizing the popular Hasbro board game Operation, which requires student involvement in the entire research process. Highlighted are ways this activity uncovers a number of research issues. A number of categorical and continuous variables are collected, making the activity amenable to a variety of statistical investigations and thus easy to imbed into any curriculum. Designed to mimic a real-world research scenario, this fun activity provides a guided yet flexible research experience from start to finish.
Allan Rossman and Beth Chance, Cal Poly - San Luis Obispo
Tuesday, October 27, 2015 - 2:00pm
We present an activity for introducing students to the concept of power and factors that influence power. The activity asks students to use a simulation-based approach, with an applet available here http://www.rossmanchance.com/applets/power.html to investigate how likely a baseball player would be to convince a manager that he has improved his probability of getting a hit.
Leigh M. Harrell-Williams, University of Memphis and Rebecca L. Pierce, Ball State University
Wednesday, October 21, 2015 - 12:00pm
Based on our March 2015 JSE paper "Identifying Statistical Concepts Associated with High and Low Levels of Self-Efficacy to Teach Statistics in Middle Grades,” we discuss the results of a Rasch modeling analysis of pre-service mathematics teacher responses to the middle grades Self-Efficacy to Teach Statistics (SETS) instrument. We share how we used Rasch measurement theory to develop the middle grades SETS instrument to measure pre-service teachers’ self-efficacy to teach topics at GAISE levels A and B as well as K–8 CCSSM statistics topics. SETS items ask teachers to rate their self-efficacy to teach a particular concept on a Likert scale from 1 (“not confident at all”) to 6 (“completely confident”). From data collected at four public institutions of higher education in the United States, we discuss what statistics topics pre-service teachers felt the most (or least) efficacious about and how that informs our continuing work.
Rob Erhardt and Michael Shuman, Wake Forest University
Wednesday, September 16, 2015 - 12:00pm
We describe the assistive technologies used to accommodate a blind student who took a second course in statistics at Wake Forest University. The course covered simple and multiple regression, model diagnostics, model selection, data visualization, and elementary logistic regression. These topics required that the student both interpret and produce three sets of materials: mathematical writing, computer programming, and visual displays of data. We relied heavily on integrating the use of multiple existing technologies. Specifically, this talk will detail the extensive use of screen readers, LaTeX, a modified use of R and the BrailleR package, a desktop Braille embosser, and a modified classroom approach.
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?
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.