Webinars

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.

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.

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

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.

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