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.
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.
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.
Jacopo Di Iorio (Penn State University)
Tuesday, March 22, 2022 - 4:00pm ET
This month, we highlight the JSDSE article, How to Get Away With Statistics: Gamification of Multivariate Statistics. One of the authors will discuss their attempt to teach applied statistics techniques typically taught in advanced courses, such as clustering and principal component analysis, to a non-mathematically educated audience by using four different interactive arcade games. The four games are all user-centric, score-based arcade experiences intended to be played under the supervision of an instructor. They were developed using the Shiny web-based application framework for R. In every activity students have to follow the instructions and to interact with plots to minimize a score with a statistical meaning. No knowledge, other than elementary geometry and Euclidean distance, is required to complete the tasks. Results from a student questionnaire give the authors some confidence that the experience benefits students. This fact suggests that these or similar activities could greatly improve the diffusion of statistical thinking at different levels of education.
Adam Loy (Carleton College)
Tuesday, February 15, 2022 - 4:00pm ET
This month, we highlight the article Bringing Visual Inference to the Classroom by Adam Loy in our Journal of Statistics and Data Science Education webinar series. In the classroom, educators traditionally visualize inferential concepts using static graphics or interactive apps. For example, there is a long history of using apps to visualize sampling distributions. The lineup protocol for visual inference is a recent development in statistical graphics that has created an opportunity to build student understanding. Lineups are created by embedding plots of observed data into a field of null (noise) plots. This arrangement facilitates comparison and helps build student intuition about the difference between signal and noise. Lineups can be used to visualize randomization/permutation tests, diagnose models, and even conduct valid inference when distributional assumptions break down. In this webinar, Adam will introduce lineups and discuss how he uses it in his introductory statistics class.
Eric Vance (University of Colorado Boulder)
Tuesday, January 25, 2022 - 4:00pm ET
This month, we highlight the article Using Team-Based Learning to Teach Data Science by Eric Vance in our Journal of Statistics and Data Science Education webinar series. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. In this webinar, he will describe the essential elements of TBL and answer questions about this appealing pedagogical strategy.
Eric A. Vance is an Associate Professor of Applied Mathematics, the Director of the Laboratory for Interdisciplinary Statistical Analysis (LISA) at the University of Colorado Boulder, and the Global Director of the LISA 2020 Network, which comprises 35 statistics and data science collaboration laboratories in 10 developing countries. He is a Fellow of the American Statistical Association (ASA) and winner of the 2020 ASA Jackie Dietz Award for the best paper in the (then) Journal of Statistics Education for "The ASCCR Frame for Learning Essential Collaboration Skills."
Dr. Philip M. Sedgwick, St. George’s, University of London, London UK
Wednesday, November 17, 2021 - 1:00pm ET
Null hypothesis significance testing (NHST) with a critical level of significance of 5% (P<0.05) has become the cornerstone of research in the health sciences, underpinning decision making. However, considerable debate exists about its value with claims it is misused and misunderstood. It has been suggested it is because NHST and P-values are too difficult to teach, and encourage dichotomous thinking in students. Consequently, as part of statistics reform it has been proposed NHST should no longer be taught in introductory courses. However, this presentation will consider if the misuse of NHST principally results from it being taught in a mechanistic way, along with claims to knowledge in teaching and erosion of good practice. Whilst hypothesis testing has shortcomings, it is advocated it is an essential component of the undergraduate curriculum. Students’ understanding can be enhanced by providing philosophical perspectives to statistics, supplemented by overviews of Fisher’s and Neyman-Pearson’s theories. This helps the appreciation of the underlying principles of statistics based on uncertainty and probability, plus the contrast of statistical with contextual significance. Moreover, students need to appreciate when to use NHST rather than being taught it as the definitive approach of drawing inferences from data.
Julia Polak (University of Melbourne) & Di Cook (Monash University)
Tuesday, November 16, 2021 - 5:00pm ET
In the November CAUSE/Journal of Statistics and Data Science Education webinar series, we have invited the authors of this recently published paper to share their experiences in running data competitions as part of classes on statistical learning. Kaggle is a data modeling competition service, where participants compete to build a model with lower predictive error than other participants. Several years ago Kaggle released a simplified service that is ideal for instructors to run competitions in a classroom setting. This webinar describes the results of an experiment to determine if participating in a predictive modeling competition enhances learning. The evidence suggests it does. In addition, students were surveyed to examine if the competition improved engagement and interest in the class. The authors will also discuss the main issues to consider when setting up a data competition in a class, including the technical aspects of using the Kaggle InClass platform.
Julia Polak is a lecturer in Statistics at the University of Melbourne. She has a broad range of research interests including nonparametric methods, forecasting and data visualisation. In addition, Julia has many years of experience in teaching statistics and data science for different audience.
Di Cook is a Professor in Econometrics and Business Statistics at Monash University in Melbourne. Her research is in the area of data visualisation, especially the visualisation of high-dimensional data using tours with low-dimensional projections, and projection pursuit. A current focus is on bridging the gap between exploratory graphics and statistical inference.
Tim Arnold (SAS Institute); Joan Garfield (Professor Emeritus of University of Minnesota); Jeff Witmer (Oberlin College)
Tuesday, October 19, 2021 - 4:00pm ET
In the October CAUSE/Journal of Statistics and Data Science Education webinar series, we will take a step back in time to talk with some of the founders of what was initially the "Journal of Statistics Education" and will be publishing its 30th volume in 2022.
In 1992, Daniel Solomon and colleagues organized a conference at North Carolina State University to explore the idea of an “Electronic Journal: Journal of Statistics Education”. Many ideas and considerable enthusiasm flowed.
The first issue of JSE was published in 1993 under the editorship of the late Jackie (E. Jacquelin) Dietz and managing editorship of J. Tim Arnold. Other contributing editors included Joan Garfield, Robin Lock, and Jeff Witmer. The inaugural issue included, among other things, an interview with Fred Mosteller, the structure and philosophy of the journal, and Joan Garfield’s widely cited paper “Teaching statistics using small-group cooperative learning”.
In this webinar, we will have a chance to hear from some of the founders about their vision for the journal from three decades ago, their reflections on what has transpired since then, and their prognostications for the future.
Tim Arnold is a Principal Software Developer at the SAS Institute. He served as the founding managing editor of JSE.
Joan Garfield is Professor Emeritus of the Department of Educational Psychology at the University of Minnesota. Joan served alongside the late J. Laurie Snell as co-editor of JSE’s “Teaching Bits, a Resource for Teachers of Statistics”.
Jeff Witmer is Professor of Mathematics at Oberlin College and is the current editor of the Journal of Statistics and Data Science Education. Jeff was a founding Associate Editor for JSE.
Useful (but not required) background reading includes:
Arnold: Structure and philosophy of the Journal of Statistics Education, https://www.tandfonline.com/doi/full/10.1080/10691898.1993.11910456
Rossman and Dietz: Interview with Jackie Dietz, https://www.tandfonline.com/doi/abs/10.1080/10691898.2011.11889616
Emily Griffith (North Carolina State University), Megan Higgs (Critical Inference LLC), and Julia Sharp (Colorado State University)
Tuesday, September 21, 2021 - 4:00pm ET
In the September CAUSE/Journal of Statistics and Data Science Education webinar series, we talk with Julia Sharp, Emily Griffith, and Megan Higgs, the co-authors of a forthcoming JSDSE paper entitled "Setting the stage: Statistical collaboration videos for training the next generation of applied statisticians" (https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1934202).
Collaborative work is inherent to being a statistician or data scientist, yet opportunities for training and exposure to real-world scenarios are often only a small part of a student’s academic program. Resources to facilitate effective and meaningful instruction in communication and collaboration are limited, particularly when compared to the abundant resources available to support traditional statistical training in theory and methods. This paper helps fill the need for resources by providing ten modern, freely-available videos of mock collaborative interactions, with supporting discussion questions, scripts, and other resources. Videos are particularly helpful for teaching communication dynamics. These videos are set in the context of academic research discussions, though the scenarios are broad enough to facilitate discussions for other collaborative contexts as well. The videos and associated resources are designed to be incorporated into existing curricula related to collaboration.
Julia Sharp is an associate professor of statistics and the Director of the Graybill Statistics and Data Science Laboratory at Colorado State University. Julia is a widely recognized expert in statistical collaboration and recently was awarded the Outstanding Mentor Award from ASA's Section on Statistical Consulting. When she is not working, Julia enjoys baking, hiking, and enjoying the company of family and friends.
Emily Griffith is an associate research professor of statistics at North Carolina State University. She is also a Fellow in the Office of Research Innovation working on development and strategy to further innovation in the university’s data sciences initiatives. In her free time, Emily enjoys running (even in the summer in NC), cooking, and hanging out with her family.
Megan Higgs has worked as a collaborative statistician in academia and private industry, and is now working independently as Critical Inference LLC and writing posts for a blog of the same name. She currently volunteers as editor of the International Statistical Institute’s “Statisticians React to the News” blog and serves on the ASA’s Climate Change Committee. Megan loves spending time with her family and pets in Montana.