Webinars

  • Designing a Large, Online Simulation-Based Introductory Statistics Course

    Ella Burnham (Gustavus Adolphus College), Erin Blankenship (University of Nebraska-Lincoln, and Sydney Brown (University of Nebraska-Lincoln)
    Tuesday, April 18, 2023 - 4:00pm ET
    This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, we highlight the article, Designing a Large, Online Simulation-Based Introductory Statistics Course. The authors designed an asynchronous undergraduate introductory statistics course that focuses on simulation-based inference at the University of Nebraska-Lincoln. In the webinar presentation, the authors plan to describe the process they used to design the course, as well as the structure of the course. They will also discuss feedback and comments they received from students on the course evaluations and will reflect on the course after teaching it for the past three years. Their goal is to provide useful tips and ideas for instructors who have developed or are developing their own asynchronous introductory course. And while they emphasized simulation-based inference in their own course, they believe that many of the design features of this course may be useful for those using a traditional approach to inference in their introductory courses.    Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2087810
  • Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses

    Mine Dogucu (University of California Irvine/University College London)
    Tuesday, March 21, 2023 - 4:00pm ET
    This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, Mine Dogucu will discuss the article, Framework for Accessible and Inclusive Teaching Materials for Statistics and Data Science Courses. The paper, coauthored by Alicia Johnson and Miles Ott, argues that despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. Thus, this pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. And the authors of the paper argue it is critical that, as statistics and data science educators of the next generation, we center accessibility and inclusion throughout our curriculum, classroom environment, modes of assessment, course materials, and more. In the webinar, with some common strategies applied across these areas, Dr. Dogucu will present a framework for developing accessible and inclusive course materials (e.g., in-class activities, course manuals, lecture slides, etc.), with examples drawn from the authors’ experience co-writing a statistics textbook. This framework establishes a structure for holding ourselves as educators accountable to these principles.
  • Exploring the Use of Statistics Curricula with Annotated Lesson Notes

    Jennifer Green (Michigan State), Liz Arnold (Colorado State)
    Tuesday, February 21, 2023 - 4:00pm ET
    Abstract: This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, we highlight the article, Exploring the Use of Statistics Curricula with Annotated Lesson Notes. In K–12 statistics education, there is a call to integrate statistics content standards throughout a mathematics curriculum and to teach these standards from a data analytic perspective. Annotated lesson notes within a lesson plan are a freely available resource to provide teachers support when navigating potentially unfamiliar statistics content and teaching practices. In their research, Dr. Green and Dr. Arnold identified several types of annotated lesson notes, created two statistics lesson plans that contained various annotated lesson notes, and observed secondary mathematics teachers implement the lesson plans in their intermediate algebra courses. They then investigated how two teachers’ instructional actions compared to what was prescribed in the annotated lesson notes. They found ways in which the teachers’ instructional actions, across their differing contexts, aligned with, varied from, or adapted to the annotated lesson notes. During the webinar they will outline their research and highlight affordances and limitations of annotated lesson notes for statistics instruction, as well as offer recommendations for those who create statistics curricula with annotated lesson notes.
  • Teaching in the Health Sciences: Is there a Viable Teaching Career Path?

    Amy Nowacki (CCHS), Amanda Ellis (UK), Steve Foti (UF), Steve Grambow (DU), Matt Hayat (GSU), James Odei (OSU) and Matt Zawistowski (MICH)
    Tuesday, January 31, 2023 - 3:00pm ET
    Career pathways for collaborative biostatisticians, where the primary focus is collaborative research, have been established in many biostatistics departments and research organizations in the last decade or so. Comparable career pathways for teaching biostatisticians, where the primary focus is teaching and teaching-related research, are much rarer, although they are becoming more common in statistics departments. These positions go by a variety of names including Teaching Professor, Professor of the Practice, or Clinical Professor. In this webinar a panel of faculty in teaching-focused positions in biostatistics will discuss the opportunities and challenges for such positions. We will discuss how common teaching-focused positions are; the typical position responsibilities, advancement opportunities, and evaluation metrics; the value of these positions for their institutions; and the barriers to their implementation. This webinar will interest biostatisticians currently in or considering teaching-focused positions, PhD students and postdocs curious about these types of positions, as well as department heads thinking about how such positions could be structured.
  • SCRATCH to R: Toward an Inclusive Pedagogy in Teaching Coding

    Shu-Min Liao (Amherst College)
    Tuesday, January 17, 2023 - 4:00pm ET
    This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, we highlight the research article, SCRATCH to R: Toward an Inclusive Pedagogy in Teaching Coding. In the webinar, Shu-Min Liao will introduce SCRATCH, a kid-friendly visual programming language developed by the Media Lab at MIT. SCRATCH was designed to introduce programming to children and teens in a “more thinkable, more meaningful, and more social” way. Although it was initially intended for K-12 students, educators have used it for higher education as well, and found it particularly helpful for those who haven’t had the privilege of learning coding before college. In this presentation, Dr. Liao will discuss using SCRATCH as a gateway to learning R in introductory or intermediate statistics courses. She will explain the design of her current project and share observations from a pilot study in a liberal arts college with 39 students who had diverse coding experiences. She found that the most disadvantaged students were not those with no coding experience, but those with poor prior coding experience or with low coding self-efficacy. This innovative SCRATCH-to-R approach also offers instructors a pathway toward an inclusive pedagogy in teaching coding. Article: https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2090467
  • The growing importance of reproducibility and responsible workflow in the data science and statistics curriculum

    Aneta Piekut (University of Sheffield), Colin Rundel (Duke University), Micaela Parker (Academic Data Science Alliance), Nicholas J. Horton (Amherst College), and Rohan Alexander (University of Toronto)
    Tuesday, December 13, 2022 - 4:00pm ET
    Many new principles and standards have been developed to facilitate cultural changes in fostering reproducible research, but less so has been done in teaching. To highlight work in this important and developing area, the Journal of Statistics and Data Science Education invited papers related to "Teaching reproducibility and responsible workflow". The November 2022 issue of the journal is devoted to this topic (see https://www.tandfonline.com/toc/ujse21/30/3). We are excited by the opportunities and options brought forward in these 11 papers. This webinar will include an overview of the special issue that is intended to provide motivation, guidance, and examples that help the data science and statistics education community better inculcate these increasingly important research-based practices. The webinar will include an opportunity for Q&A with the audience focused on next steps and ways to move forward.
  • Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings

    Kirsten Doehler (Elon University)
    Tuesday, November 15, 2022 - 4:00pm ET
    This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, we highlight the article, Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings. A Statistics Practicum course can be offered as another option besides an internship or research experience for students to fulfill a required statistics major capstone experience. This webinar will discuss the first and fourth offering of this practicum course, which provided a unique perspective on the initial implementation of the course and its development over time. The course offered students opportunities to carry out statistical consulting projects with external clients. Students were given multiple reflection assignments throughout the course. Challenges of the projects were discussed in the reflections, which included issues of data cleaning and analysis. Students also responded to both Likert-scale and open-ended questions on an end of semester survey. These responses provided information on sentiment regarding the consulting projects and perceived usefulness of various components of the Statistics Practicum course. Both student reflection assignments and survey responses were analyzed as part of this study. Explanations of the thought processes that went into setting up and running the course, as well as advice and suggestions for course improvements and successful administration, will be discussed. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2044943
  • Integrating Data Science Ethics Into an Undergraduate Major

    Benjamin S. Baumer (Smith College); Katherine M. Kinnaird (Smith College)
    Tuesday, July 19, 2022 - 1:30pm ET
    This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar series, we highlight the research article, Integrating Data Science Ethics Into an Undergraduate Major.  In the webinar, the presenters will present a programmatic approach to incorporating ethics into an undergraduate major in statistical and data sciences. They will discuss departmental-level initiatives designed to meet the National Academy of Sciences recommendation for integrating ethics into the curriculum from top-to-bottom as their majors progress from the introductory courses to the senior capstone course, as well as from side-to-side through co-curricular programming. They will also provide six examples of data science ethics modules used in five different courses at their liberal arts college, each focusing on a different ethical consideration. The modules are designed to be portable such that they can be flexibly incorporated into existing courses at different levels of instruction with minimal disruption to syllabi. The presenters will connect their efforts to a growing body of literature on the teaching of data science ethics, present assessments of their effectiveness, and conclude with next steps and final thoughts. Article: https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2038041 Slides https://beanumber.github.io/talks/jsdse2022/data_ethics.html
  • Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning

    Alex Reinhart (Carnegie Mellon University), Ciaran Evans (Wake Forest University), and Amanda Luby (Swarthmore College)
    Tuesday, June 28, 2022 - 4:00pm ET
    This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar, we highlight the research article, Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning,  in our Journal of Statistics and Data Science Education webinar series. In the webinar, the presenters will discuss think-aloud interviews, in which students narrate their reasoning in real time while solving problems. Think-aloud interviews are a valuable but underused tool for statistics education research. In this webinar, the presenters suggest possible use cases for think-alouds and summarize best practices for designing think-aloud interview studies. They hope that their overview of think-alouds encourages more statistics educators and researchers to begin using this method.
  • Building a Multiple Linear Regression Model With LEGO Brick Data

    Anna Peterson and Laura Ziegler, Iowa State University
    Tuesday, April 19, 2022 - 4:00pm ET
    This month, we highlight the Datasets and Stories article, Building a Multiple Linear Regression Model with LEGO Brick Data,  in our Journal of Statistics and Data Science Education webinar series. In the webinar, they present an innovative activity that uses data about LEGO sets to help students self-discover multiple linear regressions. During the activity, instructors guide students to predict the price of a LEGO set posted on Amazon.com (Amazon price) using LEGO characteristics such as the number of pieces, the theme (i.e., product line), and the general size of the pieces. By starting with graphical displays and simple linear regression, students are able to develop additive multiple linear regression models as well as interaction models to accomplish the task. They conclude with reflections of past classroom experiences. https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1946450

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