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  • Teacher Education Curriculum Materials that Develop Statistical Knowledge for Teaching

    Stephanie Casey, Andrew Ross (Eastern Michigan University)
    Tuesday, February 9, 2021 - 2:00pm
    To event remaining 15 days
    Statistics is more important than ever in today's data-driven world. This is reflected in the increased level of statistics understanding expected of K-12 students according to the CCSS-M and state-level standards. To develop middle and high school teachers' statistical knowledge for teaching, the MODULE(S^2) project has created curriculum materials for use in introductory statistics course(s) that preservice secondary teachers take. The materials develop preservice teachers’  subject matter and pedagogical content knowledge for teaching statistics as well as their equity literacy. In this webinar, we will provide an introduction to these materials including examples of statistical tasks and classroom videos from the materials. Alignment of these materials with ASA’s GAISE, ASA’s Statistical Education of Teachers report, and the Association of Mathematics Teacher Educator's Standards for Preparing Teachers of Mathematics will be highlighted. Also, we are recruiting faculty to be piloters for the materials. To find sample materials, visit, and to indicate you are interested in piloting, please fill out the form at
  • Students Building Shiny Apps: 
An Update on the 
BOAST Project

    Neil Hatfield, Leah Hunt, Ethan Wright, Gonghao Liu, Xigang Zhang, & Zeyuan (Primo) Wang (Penn State University)
    Tuesday, December 8, 2020 - 2:00pm
    For the past four years, teams of Penn State statistics and data science undergraduates have spent the summer and fall developing apps for teaching statistical concepts. Their work has culminated in over 60 apps as part of the Book of Apps for Statistics Teaching (BOAST). This webinar will share some details of the project and give some of the students the opportunity to highlight some of the newest apps they have developed. Visit:
  • Causal Inference: Why We Should and How We Can Teach it in Introductory Courses

    Karsten Lübke (FOM University)
    Tuesday, June 9, 2020 - 2:00pm
    We are living in a world full of multivariate observational data. Qualitative assumptions about the data generating process, operationalised in simple directed acyclic graph can help students to understand multivariate phenomena like Simpson's or Berkson's paradox, confounding and bias. By teaching causal inference the introductory course can overcome the mantra "correlation does not imply causation". The webinar discusses some motivation as well as teaching ideas and the integration in the curriculum.
  • The Art of Storytelling: Enhancing Graduate Students' Oral Communication Skills

    Jennifer Green (Montana State University)
    Tuesday, February 11, 2020 - 2:00pm
    In this webinar, I will discuss a novel oral communication curriculum we developed and use with graduate students to help them communicate their scientific work with others. I'll use examples of how the students leverage elements of storytelling, stage presence, and improvisational skills to more effectively connect with and captivate audiences as they convey their research. We will also explore how these ideas can transfer into our own work, building a shared knowledge of how we can support students' (and our own) development of oral communication skills.
  • Introducing think-aloud interviews as a tool to explore student statistical reasoning

    Mikaela Meyer & Ciaran Evans (Carnegie Mellon University)
    Tuesday, December 10, 2019 - 2:00pm
    Think-aloud interviews with students can be used to detect specific misconceptions and understand how students reason about statistical questions. Data from think-aloud interviews can then be used to develop conceptual assessments, design new teaching strategies, or suggest further experiments to learn how students think about statistics. In this webinar, we will discuss the benefits of using think-aloud interviews to develop conceptual assessments and the experience we have had using think-aloud interviews in two introductory-level statistics courses.
  • Causal Inference in Introductory Statistics Courses: Why, What, and How?

    Kevin Cummiskey & Bryan Adams (West Point)
    Tuesday, October 8, 2019 - 4:00pm
    In this talk, we will discuss why causal inference concepts align well with recommendations for introductory statistics courses and propose topics appropriate for such courses. In addition, we will highlight some resources for instructors interested in teaching causal inference, including a classroom activity we developed based on a popular dataset investigating the effects of youth smoking on lung function.
  • Capstone assessment for the undergraduate statistics major

    Matt Beckman (Pennsylvania State University)
    Tuesday, September 10, 2019 - 2:00pm
    This work introduces new assessment tools to measure learning outcomes of students in undergraduate statistics programs (e.g. majors) against the competencies recommended in the (2014) ASA Guidelines for Undergraduate Programs in Statistical Sciences. In short, these assessment tools seek to (1) measure student learning outcomes with respect to program objectives; (2) discover whether students are gaining additional relevant competencies not explicitly included in the program/major through extracurricular experiences; (3) facilitate comparisons across years and institutions to benefit continuous improvement of the program/major. This webinar presents uses and results after piloting with Senior/Capstone undergraduate statistics students shortly before graduation at four different institutions around the US.
  • Statistical Computing: How Does it Impact Statistical Thinking and Problem Solving?

    Victoria Woodard (Notre Dame University)
    Tuesday, August 20, 2019 - 2:00pm
    In this webinar, I will discuss findings from a qualitative study that was conducted based on written work and task-based interviews of students completing a second course in statistics. In particular, I will focus on three major topics: The methodology used for analyzing our qualitative data, Beginning to define the relationship that was observed between a student’s ability to think statistically while utilizing statistical computing tools and Observations about how students solve problems while utilizing statistical computing tools.
  • Preparing teachers to teach statistics with online learning modules

    Hollylynne Lee (NC State University)
    Tuesday, July 9, 2019 - 2:00pm
    As statistics and data science become more important and prominent in secondary schools, we need more teachers ready to teach statistics in data-rich ways. Enhancing Statistics Teacher Education through E-Modules [ESTEEM] is an NSF-funded project to develop and disseminate research-based online learning materials to be used in teacher education courses ( In this webinar, participants will be introduced to our online materials, including videos of students and teachers engaged in rich statistics tasks, interviews with experts educators, and investigations with a free online tool CODAP. Different implementation models used and evaluation results will be shared. Participants will learn how to register for free access to materials and download all materials in common Learning Management System formats (Moodle, Canvas, Blackboard) that are ready for upload into their own courses.
  • A Course Community for a Culture Change

    Lisa Green (Middle Tennessee State University)
    Tuesday, June 11, 2019 - 2:00pm
    At Middle TN State University (MTSU), the introductory statistics class is taught by a diverse set of instructors. The ideal teacher for this course would be both statistically trained and experienced in the classroom. However, we often have people who are experienced teachers, but not statistically trained, like instructors with a Master’s in mathematics. Or statistically trained, but not experienced teachers, like graduate students in our Biostatistics program. When we decided to change the teaching method of this class to focus on more active-learning and less lecture-based classes, we had to consider the various types of instructors, and reasons they might feel uncomfortable with this change. We formed a course community in which all the instructors of this course were invited to meet approximately every two weeks during the semester before the change and the semester in which the change happened. This webinar will discuss how the course community functioned and the effects that it had on the teaching of this course.