Webinars

In this November edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Simulation-Based Inference: Random Sampling vs. Random Assignment? What Instructors Should Know. “Simulation-based inference” is often considered a pedagogical strategy for helping students develop inferential reasoning, for example, giving them a visual and concrete reference for deciding whether the observed statistic is unlikely to happen by chance alone when the null hypothesis is true. In this webinar, the presenters highlight for teachers some implications of different simulation strategies when analyzing two variables. In particular, does it matter whether the simulation models random sampling or random assignment? They present examples from comparing two means and simple linear regression, highlighting the impact on the standard deviation of the null distribution. They also highlight some possible extensions that simulation-based inference easily allows. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2333736  

In this October edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article Philosophy within Data Science Ethics Courses. There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, the speakers present a framework for bringing together key data science practices with ethics topics. The ethics topics were collated from sixteen data science ethics courses with public-facing syllabi and reading lists. The speakers encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which are rife with potentially morally charged decision points. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2394542   

In this September edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article What They Remember May Not Be What They Understand: A Study of Mnemonic Recall and Performance by Introductory Statistics Students. The speakers will discuss a large-scale (n = 1323) survey of mnemonic recall, self-reported familiarity, cued explanation, and application by introductory statistics students that was conducted at a large research university in the southeastern United States. The students were presented 14 mnemonics during the fall 2017 term. Different nonoverlapping cohorts of students were asked at different time points to complete a survey about mnemonic use. At each time point, the students were asked to recall any mnemonic that they remembered, explain the mnemonic when cued, self-report their degree of familiarity, and apply the mnemonic. Of the 14 mnemonics, acronym-type mnemonics were recalled more frequently, but longer phrase-type mnemonics were explained and applied more often. These findings suggest that instructors should provide scaffolding to move a student from recalling a mnemonic to using a mnemonic toward successful completion of the statistics problem at hand. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2334905 

In 2022, the Journal of Statistics and Data Science Education (JSDSE) instituted augmented requirements for authors to post deidentified data and code underlying their papers. These changes were prompted by an increased focus on reproducibility and open science, and a recent review of data availability practices noted that "such policies help increase the reproducibility of the published literature, as well as make a larger body of data available for reuse and re-analysis" (PLOS ONE, 2024). In this talk, Nicholas Horton and Sara Stoudt present their recent editorial for JSDSE, discussing the motivation and process for sharing deidentified data and code. Because institution, environment, and students differ across readers of the journal, it is especially important to facilitate the transfer of a journal article's findings to new contexts. This process may require digging into more of the details, including the deidentified data and code. The presenters will present a review of why the requirements for code and data sharing were instituted, summarize ongoing trends and developments in open science, discuss options for data and code sharing, and share advice for authors. Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2364737

In this June edition of the JSDSE/CAUSE webinar series, we highlight the 2024 article The Teaching of Introductory Statistics: Results of a National Survey. The authors describe their use of the updated Statistics Teaching Inventory (STI) to examine the current state of the curricular and instructional practices being used by U.S.-based, college-level introductory statistics instructors. They explore the extent to which instructors report that their introductory statistics courses are aligned with recommended practices as outlined by the 2016 GAISE College Report. Data were collected from a sample of college-level U.S.-based, college-level introductory statistics instructors. Results based on 228 usable responses indicated that instructors, by-and-large, tended to be following the GAISE recommendations, especially related to content. However, courses may not yet be aligned with newer content recommendations (e.g., provide students opportunities to work with multivariate data), and there is still a large percentage of instructors that are not embracing student-oriented pedagogies and assessment methods.

This webinar shares experiences and guidance for development of assessment tasks and scoring rubrics associated with open-ended, free response tasks.  The session also draws comparisons between implications when the assessment is used in a classroom vs research context.  Acknowledgment: this webinar was prepared and delivered with the support of NSF Project CLASSIFIES (DUE: IUSE #2236150).  Disclaimer: AP® is a trademark registered by College Board, which is not affiliated with and does not endorse this presentation or Iowa State University’s events, programs, etc

In this May edition of the JSDSE/CAUSE webinar series, we highlight the 2023 article: Culturally Relevant Data in Teaching Statistics and Data Science Courses. The authors will discuss how to make data more meaningful to students through the choice of data and the activities they use to draw upon students' lived experiences. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics education they develop the idea of culturally relevant data, which they use to implement culturally relevant pedagogy in teaching data-intensive courses, leveraging the centrality of context through data in both statistics and data science to engage students particularly from historically marginalized groups in STEM. They provide suggestions as to ways of finding or creating culturally relevant data and using them, and they also present findings from pilot work on implementing these data in statistics courses.   Article Link: https://www.tandfonline.com/doi/full/10.1080/26939169.2023.2249969   Please join us! Ciaran Evans

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

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

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