Michael D. Swartz, PhD – Department of Biostatistics and Data Science at the University of Texas Health Science Center at Houston
Friday, June 11, 2021 - 2:00pm ET
The idea of developing a rubric for assessments or flipping lectures in an Applied Biostatistics (or even Applied Statistics) classroom can be overwhelming, but it does not have to be. I will lead a discussion introducing several ideas for building a rubric for statistics assignments and exams, and flipping parts of a lecture to combine traditional lecture with interactive components to fully engage students to enhance their learning in the classroom or live synchronous sessions (like teaching through Webex or Zoom) using polling software like PollEverywhere. The polling software strategy I introduce will also provide instructors real-time feedback regarding students’ current comprehension of the material. One of the techniques can also be modified to increase engagement for an online only format (pre-recorded lectures). Attendees who consider themselves beginners with respect to rubrics or flipped classrooms as well as those who consider themselves more experienced are welcome to this webinar.
Pip Arnold (New Zealand) & Chris Franklin (ASA K-12 Statistics Ambassador/ASA Fellow/UGA Emerita)
Tuesday, May 25, 2021 - 4:00pm ET
In the April CAUSE/Journal of Statistics and Data Science Education webinar series, we discuss "What Makes a Good Statistical Question?" with Pip Arnold & Christine Franklin, the co-authors of a forthcoming paper in JSDSE (https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1877582).
The statistical problem-solving process is key to the statistics curriculum at the school level, post-secondary, and in statistical practice. The process has four main components: Formulate questions, collect data, analyze data, and interpret results. The Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education (GAISE) emphasizes the importance of distinguishing between a question that anticipates a deterministic answer and a question that anticipates an answer based on data that will vary, referred to as a statistical question. This paper expands upon the Pre-K-12 GAISE distinction of a statistical question by addressing and identifying the different types of statistical questions used across the four components of the statistical problem-solving process and the importance of interrogating these different statistical question types. Since the publication of the original Pre-K-12 GAISE document, research has helped to clarify the purposes of questioning at each component of the process, to clarify the language of questioning, and to develop criteria for answering the question, "What makes a good statistical question?"
Pip Arnold is a statistics educator who also sometimes masquerades as a mathematics educator. Her continuing interests include statistical questions, working to support with K-10 teachers in developing their statistical content knowledge and looking at ways to authentically integrate statistics across the curriculum. Pip has been developing a teacher's resource to support the teaching of statistics from K-10 for New Zealand teachers, based on the PPDAC statistical enquiry cycle that is the basis of statistical problem-solving in New Zealand.
Christine (Chris) Franklin is the ASA K-12 Statistics Ambassador, an ASA Fellow, and UGA Emerita Statistics faculty. She is the co-author of two introductory statistics textbooks, chair for the ASA policy documents Pre-K-12 GAISE (2005) and Statistical Education of Teachers (2015), and co-chair for the recently published Pre-K-12 GAISE II. She is a former AP Statistics Chief Reader and a past Fulbright scholar to NZ, where she and Pip began having many conversations about the role of questioning in the statistical problem-solving process.
Andrew Zieffler (University of Minnesota) & Nicola Justice (Pacific Lutheran University)
Tuesday, April 27, 2021 - 4:00pm ET
Classification trees and other algorithmic models are an increasingly important part of statistics and data science education. In the April CAUSE/Journal of Statistics and Data Science Education webinar series, we will talk with Andrew Zieffler and Nicola Justice, two of the co-authors of the forthcoming JSDSE paper entitled “The Use of Algorithmic Models to Develop Secondary Teachers' Understanding of the Statistical Modeling Process”: https://www.tandfonline.com/doi/full/10.1080/26939169.2021.1900759
Statistical modeling continues to gain prominence in the secondary curriculum, and recent recommendations to emphasize data science and computational thinking may soon position algorithmic models into the school curriculum. Many teachers’ preparation for and experiences teaching statistical modeling have focused on probabilistic models. Subsequently, much of the research literature related to teachers’ understanding has focused on probabilistic models. This study explores the extent to which secondary statistics teachers appear to understand ideas of statistical modeling, specifically the processes of model building and evaluation, when introduced using classification trees, a type of algorithmic model. Results of this study suggest that while teachers were able to read and build classification tree models, they experienced more difficulty when evaluating models. Further research could continue to explore possible learning trajectories, technology tools, and pedagogical approaches for using classification trees to introduce ideas of statistical modeling.
Andrew Zieffler is a Senior Lecturer and researcher in the Quantitative Methods in Education program within the Department of Educational Psychology at the University of Minnesota. His scholarship focuses on statistics education. His research interests have recently focused on teacher education and on how data science is transforming the statistics curriculum. You can read more about his work and interests at https://www.datadreaming.org/.
Nicola Justice studies how students and teachers learn statistics. As an assistant professor at Pacific Lutheran University, her passion is to help students develop into skillful and ethical data storytellers. When not teaching or learning, she likes to get outside with her family: hiking, exploring, and throwing rocks in water.
Subha Nair (HHMSPB NSS College for Women); Satheesh Kumar (University of Kerala); Asha Gopalakrishnan (Cochin University of Science and Technology); Mousumi Banerjee (UMICH); Kevin Wilson (Newcastle University); Sahir Bhatnagar (McGill University)
Tuesday, March 30, 2021 - 10:00am ET
A new form of pedagogical approach was thrust upon us by the pandemic – online classrooms; a concept that was never experienced or experimented to the extent that was witnessed in the past few months. The challenges involved in online teaching are many, especially for a discipline like statistics which is essential for students undergoing courses in science, health science as well as social sciences. The challenges can be anything, including difficulty in comprehending foundational concepts through virtual classrooms, lack of availability of technical tools such as electronic gadgets or internet coverage, lack of online teaching tools and resources, absence of appropriate technical knowhow which can hinder the ease of communication between the facilitators and students, and lack of appropriate evaluation of student performances. The extent of these challenges may vary from region to region and would depend upon socio-economic profiles of the places. However, the global academic fraternity in the statistics community is committed to effective dissemination of statistics content and knowledge to students from multiple disciplines amid these changed circumstances. In such a scenario, it will be both important as well as informative to have a platform for experience sharing of experts from around the world. This will not only help us exchange information regarding multiple academic approaches and evaluation aids successfully implemented by the statistics fraternity, but also provide significant insights into the availability of shared resources and identify what worked well in different geographical regions.
Mine Dogucu (UC Irvine) & Albert Y. Kim (Smith College)
Tuesday, March 23, 2021 - 4:00pm ET
In the March CAUSE/Journal of Statistics and Data Science Education webinar series we will discuss two related papers on data ingestation, data collection, and data analysis.
Mine Dogucu (UC Irvine) will discuss her paper "Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities" (https://github.com/mdogucu/web-scrape).
Albert Y. Kim (Smith College) will discuss his paper "'Playing the Whole Game': A Data Collection and Analysis Exercise With Google Calendar" (https://smithcollege-sds.github.io/sds-www/JSE_calendar.html)
Mine Dogucu is an Assistant Professor of Teaching in the Department of Statistics at University of California Irvine. Her work focuses on modern pedagogical approaches in the statistics curriculum, making data science education accessible, and undergraduate Bayesian education. She is the coauthor of the upcoming book Bayes Rules! An Introduction to Bayesian Modeling with R. She co-chairs the Undergraduate Statistics Project Competition and the Electronic Undergraduate Statistics Research Conference (USPROC+eUSR). She shares her thoughts about data science education on her Data Pedagogy blog.
Albert Kim is an Assistant Professor of Statistical & Data Sciences at Smith College as well as a Visiting Scholar at the ForestGEO network's Smithsonian Conservation Biology Institute (SCBI) large forest dynamics plot. His research centers on forest ecology, in particular modeling the impact of climate change on the growth of trees as well as ecological forecasting. He is a co-author of "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" (see moderndive.com).
Jingchen (Monika) Hu (Vassar College), Kevin Ross (Cal Poly - San Luis Obispo), & Colin Rundel (University of Edinburgh/Duke)
Tuesday, February 23, 2021 - 4:00pm ET
The Journal of Statistics and Data Science Education recently published a cluster of papers on Bayesian methods (https://www.tandfonline.com/toc/ujse20/28/3?nav=tocList). The Bayesian cluster includes a presentation of how and why Bayesian ideas should be added to the curriculum; guidance on how to structure a semester-long Bayesian course for undergraduates; a discussion of the ever-evolving nature of Bayesian computing; a book review; and a panel interview of several Bayesian educators.
For the February CAUSE/JSDSE webinar series, we’ve invited several authors of these provocative and informative articles to describe their work and its implications for statistics and data science education.
Jingchen (Monika) Hu is an Assistant Professor of Mathematics and Statistics at Vassar College. She teaches an undergraduate-level Bayesian Statistics course at Vassar, which is shared online across several liberal arts colleges. Her research focuses on dealing with data privacy issues by releasing synthetic data.
Kevin Ross is an Associate Professor of Statistics at Cal Poly San Luis Obispo. His research interests include probability, stochastic processes and applications as well as probability and statistics education.
Colin Rundel is a lecturer at the University of Edinburgh and an assistant professor of the practice in Statistical Science at Duke University. His research interests include applied spatial statistics with an emphasis on Bayesian statistics and computational methods.
Stephanie Casey, Andrew Ross (Eastern Michigan University)
Tuesday, February 9, 2021 - 2:00pm ET
Statistics is more important than ever in today's data-driven world. This is reflected in the increased level of statistics understanding expected of K-12 students according to the CCSS-M and state-level standards. To develop middle and high school teachers' statistical knowledge for teaching, the MODULE(S^2) project has created curriculum materials for use in introductory statistics course(s) that preservice secondary teachers take. The materials develop preservice teachers’ subject matter and pedagogical content knowledge for teaching statistics as well as their equity literacy. In this webinar, we will provide an introduction to these materials including examples of statistical tasks and classroom videos from the materials. Alignment of these materials with ASA’s GAISE, ASA’s Statistical Education of Teachers report, and the Association of Mathematics Teacher Educator's Standards for Preparing Teachers of Mathematics will be highlighted. Also, we are recruiting faculty to be piloters for the materials.
To find sample materials, visit https://modules2.com/statistics/, and to indicate you are interested in piloting, please fill out the form at https://modules2.com/use-our-materials/.
Mine Çetinkaya-Rundel (University of Edinburgh/RStudio) & Alex Reinhart (Carnegie Mellon University)
Tuesday, January 26, 2021 - 4:00pm ET
The Journal of Statistics and Data Science Education special issue on “Computing in the Statistics and Data Science Curriculum” features a set of papers that provide a mosaic of curricular innovations and approaches that embrace computing. The collected papers (1) suggest creative structures to integrate computing, (2) describe novel data science skills and habits, and (3) propose ways to teach computational thinking.
In this webinar, we've invited two authors of papers in the special issue to talk about their work and to answer questions originally posed by Nolan and Temple Lang in their 2010 TAS paper "Computing in the Statistics Curriculum":
When they graduate, what ought our students be able to do computationally, and are we preparing them adequately in this regard?
Do we provide students the essential skills needed to engage in statistical problem solving and keep abreast of new technologies as they evolve?
Do our students build the confidence needed to overcome computational challenges to, for example, reliably design and run a synthetic experiment or carry out a comprehensive data analysis?
Overall, are we doing a good job preparing students who are ready to engage in and succeed at statistical inquiry?
Neil Hatfield, Leah Hunt, Ethan Wright, Gonghao Liu, Xigang Zhang, & Zeyuan (Primo) Wang (Penn State University)
Tuesday, December 8, 2020 - 2:00pm ET
For the past four years, teams of Penn State statistics and data science undergraduates have spent the summer and fall developing apps for teaching statistical concepts. Their work has culminated in over 60 apps as part of the Book of Apps for Statistics Teaching (BOAST). This webinar will share some details of the project and give some of the students the opportunity to highlight some of the newest apps they have developed.
Douglas Landsittel (University of Pittsburgh)
Thursday, November 12, 2020 - 2:00pm ET
Many areas of clinical research, such as comparative effectiveness research and patient-centered outcomes research, strongly depend on making causal inferences from observational data. Further, these topic areas also utilize pragmatic trials and quasi-experimental designs, where consistent estimation of causal effects is also more challenging than traditional randomized controlled trials, and/or involves distinct approaches for intention-to-treat versus as-treated or per-protocol effects. While substantial literature exists on associated designs and analysis strategy, the corresponding methods are complex and not always taught in formal training, even within graduate statistics or biostatistics programs. Therefore, a critical need exists for accessible educational resources and the expansion of relevant courses and training programs. Regarding that goal, however, significant debate exists on whether these advanced methods should even be taught at all to non-statisticians, and/or researchers with more limited statistical training (e.g. a fundamental course and some background in regression). This talk proposes some possible perspectives to effectively address these concerns, while still avoiding the result of "knowing enough to be dangerous". The presenter has some related links at www.landsittellab.pitt.edu. This work was supported by AHRQ grant R25HS023185, PCORI contract R-IMC-1306-03827, and supplemental funding from the NIH/NLM grant 5 T15 LM007059-32.