• Repurposing a peer-reviewed publication to engage students in statistics: An illustration of study design, data collection, and analysis

    Ciaran Evans (Wake Forest University) and William Cipolli (Colgate University)
    Tuesday, September 19, 2023 - 4:00pm ET
    In this September edition of the JSDSE/cause webinar series, we highlight  the 2023 article: Repurposing a peer-reviewed publication to engage students in statistics: An illustration of study design, data collection, and analysis. The authors will discuss how engaging and motivating students in an undergraduate statistics courses can be enhanced by using topical peer-reviewed publications for analyses as part of course assignments. Given the popularity of on-campus therapy dog stress-reduction programs, this topic fosters buy-in from students whilst providing information regarding the importance of mental health and well-being as it impacts learning. In the webinar, the authors will describe how instructors can use a study on the benefits of human–dog interactions to teach students about study design, data collection and ethics, and hypothesis testing. The data and research questions are accessible to students without requiring detailed subject-area knowledge. Students can think carefully about how to collect and analyze data from a randomized controlled trial with two-sample hypothesis tests. Instructors can use these data for short in-class examples or longer assignments and assessments, and throughout the article and in the webinar, the authors will suggest activities and discussion questions.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2238018  
  • Playmeans: Inclusive and Engaging Data Science Through Music

    Davit Khachatryan (Babson College)
    Tuesday, May 16, 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, Dr. Davit Khachatryan discusses his web application (and paper): Playmeans. According to decades of research in educational psychology, learning is a social process that is enhanced when it happens in contexts that are familiar and relevant. But because of the skyrocketing popularity of data science, professors today often work with students coming from an abundance of academic concentrations, professional, and personal backgrounds. How can teaching account for the existing multiplicity of interests and be inclusive of diverse cultural, socioeconomic, and professional backgrounds? Music is a convenient medium that can engage and include. Enter Playmeans, a novel web application (“app”) that enables students to perform unsupervised learning while exploring music. The flexible user interface lets a student select their favorite artist and acquire, in real time, the corresponding discography in a matter of seconds. The student then interacts with the acquired data by means of visualizing, clustering, and, most importantly, listening to music—all of which are happening within the novel Playmeans app.      Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2138801
  • 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
  • Methods for Introducing the Future Public Health Workforce to Data Analysis

    Dr. Amanda Ellis, Department of Biostatistics at the University of Kentucky College of Public Health
    Wednesday, November 2, 2022 - 4:00pm ET
    The challenges of teaching introductory data analysis in an online environment are well known. These challenges can increase when the primary audience for the course are students pursuing non-quantitative degrees. In this talk, we will discuss the development of a fully online synchronous course designed for such a student audience, specifically Master of Public Health (MPH) students. Both problem-based learning and experiential learning theory methodologies informed course design. Students in the class worked individually and as team scientists to complete a data analysis project. They were exposed to data analysis elements from project initiation to dissemination while simultaneously learning methodologic concepts. Although the course was designed for MPH students, an instructor could modify the course for any cohort of students in an introductory statistics course where the focus is application and communication. Both course development and design will be discussed, and evaluations from both students and the instructor will be provided.