• Training Interdisciplinary Data Science Collaborators: A Comparative Case Study

    Eric Vance (University of Colorado Boulder) and Jessica Alzen (University of Colorado Boulder)
    Tuesday, April 9, 2024 - 4:00pm ET
    Abstract: In this April edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Training Interdisciplinary Data Science Collaborators: A Comparative Case Study.  The authors will discuss their work developing a method for teaching statistics and data science collaboration, a framework for identifying elements of effective collaboration, and a comparative case study to evaluate the collaboration skills of both a team of students and an experienced collaborator on two components of effective data science collaboration: structuring a collaboration meeting and communicating with a domain expert. Results show that the students could facilitate meetings and communicate comparably well to the experienced collaborator, but that the experienced collaborator was better able to facilitate meetings and communicate to develop strong relationships, an important element for high-quality and long-term collaboration. Further work is needed to generalize these findings to a larger population, but these results begin to inform the field regarding effective ways to teach specific data science collaboration skills.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2191666
  • Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers

    Qing Wang (Wellesley College) and Xizhen Cai (Williams College)
    Thursday, March 21, 2024 - 12:00pm ET
    In this March edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers. The authors will discuss support vector classifiers, one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is unconventional to many students in a second or third course in statistics or data science. As a result, this topic is often not as intuitive to students as some of the more traditional statistical modeling tools. In order to facilitate students’ mastery of the topic and promote active learning, the authors have developed some in-class activities and their accompanying Shiny applications for teaching support vector classifiers. The designed course materials aim at engaging students through group work and solidifying students’ understanding of the algorithm via hands-on explorations. The Shiny applications offer interactive demonstration of the changes of the components of a support vector classifier when altering its determining parameters.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2231065
  • Causal Inference Is Not Just a Statistics Problem

    Lucy D'Agostino McGowan (Wake Forest University), Travis Gerke (The Prostate Cancer Clinical Trials Consortium), and Malcolm Barrett (Stanford University)
    Tuesday, February 20, 2024 - 4:00pm ET
    In this February edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article: Causal Inference Is Not Just a Statistics Problem. The authors will discuss four datasets, similar to Anscombe’s quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an included factor. Despite the fact that the statistical summaries and visualizations for each dataset are identical, the true causal effect differs, and estimating it correctly requires knowledge of the data-generating mechanism. These example datasets can help practitioners gain a better understanding of the assumptions underlying causal inference methods and emphasize the importance of gathering more information beyond what can be obtained from statistical tools alone.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2276446
  • Coding Code: Qualitative Methods for Investigating Data Science Skills

    Allison Theobold (California Polytechnic State University, San Luis Obispo), Megan Wickstrom (Montana State University), Stacey Hancock (Montana State University)
    Wednesday, January 31, 2024 - 4:00pm ET
    Abstract: In this January edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Coding Code: Qualitative Methods for Investigating Data Science Skills.  The authors will discuss how to conceptualize and carry out a qualitative coding process with students' computing code, which allows them to explore research questions about students' learning. Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students’ programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students’ learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2277847     Please join us! Leigh Johnson
  • Implementation of Alternative Grading Methods in a Mathematical Statistics Course

    Brenna Curley, Moravian University and Jillian Downey, Gustavus-Adolphus College
    Tuesday, November 21, 2023 - 4:00pm ET
     In this November edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Implementation of Alternative Grading Methods in a Mathematical Statistics Course.  The authors will discuss how alternative grading methods, such as standards-based grading, provide students multiple opportunities to demonstrate their understanding of the learning outcomes in a course. These grading methods allow for more flexibility and help promote a growth mindset by embracing constructive failure for students. Implementation of these alternative grading methods requires developing specific, transparent, and assessable standards. The authors will also discuss that moving away from traditional methods requires a mindset shift for how both students and instructors approach assessment. While providing multiple opportunities is important for learning in any course, these methods are particularly relevant to an upper-level mathematical statistics course where topics covered often provide an additional challenge for students as they lie at the intersection of both theory and application. By providing multiple opportunities, students have the space for constructive failure as they tackle learning both a conceptual understanding of statistics and the supporting mathematical theory. In this webinar the authors will share their experiences—including both challenges and benefits for students and instructors—in implementing standards-based grading in the first semester of a mathematical statistics course (i.e., focus primarily on probability).   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2249956
  • Teaching the Difficult Past of Statistics to Improve the Future

    Lee Kennedy-Shaffer (Vassar College)
    Tuesday, October 17, 2023 - 4:00pm ET
     In this October edition of the JSDSE/cause webinar series, we highlight the 2023 article: Teaching the Difficult Past of Statistics to Improve the Future.  The author will discuss how, in recent years, the discipline of statistics has begun reckoning with its difficult history. Institutions are reconsidering names that have honored key historical figures in statistics who have deep ties to eugenics movements and racial and class prejudice. These names, however, continue to appear in our classrooms, where we teach the methods created by these individuals, raising the question of how instructors should address their legacies. Three examples of famous statisticians and their work—Francis Galton’s use of conditional probabilities to demonstrate “hereditary talent,” Karl Pearson’s attempt to quantify the intelligence of Jewish immigrant students, and Ronald A. Fisher’s creation of the analysis of variance to de-emphasize environment in human development—highlight the intimate ties between statistics and eugenics. These examples, along with a discussion of the context of these men, eugenics movements, and the statisticians and scientists who opposed their eugenic programs, can humanize the field for students, teach them about the challenges in accurate and unbiased data collection and analysis, and connect historical mistakes to contemporary ethical issues. Confronting this history in the classroom can both improve the teaching of the statistical methodologies themselves and begin a broader conversation about the role of statistics in the world.    Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2224407
  • 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.