Webinars

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

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.

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

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.

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

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. https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1997128

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. https://aloy.github.io/classroom-vizinf/

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."

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.

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. Syllabus: https://handbook.unimelb.edu.au/2017/subjects/mast90083