Webinars

The Journal of Statistics and Data Science Education special issue on “Computing in the Statistics and Data Science Curriculum” features a set of papers that provide a mosaic of curricular innovations and approaches that embrace computing. The collected papers (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking. In this webinar, we've invited two authors of papers in the special issue to talk about their work and to answer questions originally posed by Nolan and Temple Lang in their 2010 TAS paper "Computing in the Statistics Curriculum": When they graduate, what ought our students be able to do computationally, and are we preparing them adequately in this regard? Do we provide students the essential skills needed to engage in statistical problem solving and keep abreast of new technologies as they evolve? Do our students build the confidence needed to overcome computational challenges to, for example, reliably design and run a synthetic experiment or carry out a comprehensive data analysis? Overall, are we doing a good job preparing students who are ready to engage in and succeed at statistical inquiry?

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.

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.

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.

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.

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.

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.

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.

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.

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.