Allan Rossman & Beth Chance, California Polytechnic State University-San Luis Obispo
"Debating the Next BIG Thing in Teaching Statistics"
We engage in a series of debates about the next BIG thing in teaching undergraduate statistics. Inspired by David Moore's maxim that "nothing tunes the neurons like disagreement," we aim to stimulate thought and foster discussion and perhaps even provoke further debates among conference participants throughout USCOTS and afterward. We will present (at least) two sides for each of the following propositions:
- The next BIG thing will be the disappearance of textbooks from our courses.
- The next BIG thing will be the elimination of face-to-face contact among students and instructors.
- The next BIG thing will be the dropping the letters z and t from the introductory course.
- The next BIG thing will be students entering undergraduate courses with considerable knowledge of statistics.
- The next BIG thing will be research-based decisions about curriculum and pedagogy.
- The next BIG thing will be a topic to be chosen by popular vote in the pre-conference survey.
- The next BIG thing will be ... we reserve the right to think of new ideas and replace these ones until the conference begins!
About the speakers
Allan Rossman is Professor of Statistics at Cal Poly- San Luis Obispo and previously taught in the Department of Mathematics and Computer Science at Dickinson College. His Ph.D. is in Statistics, from Carnegie Mellon University. He is co-author of the Workshop Statistics series and also of Investigating Statistical Concepts, Applications, and Methods, both of which adopt an active learning approach to learning introductory statistics. He is a Fellow of the American Statistical Association and was Program Chair for the 2007 Joint Statistical Meetings. He serves as Chief Reader for the Advanced Placement program in Statistics and is Past-President of the International Association for Statistical Education. He was one of the recipients of the Mathematical Association of America's Deborah and Franklin Tepper Haimo Award for Distinguished College or University Teaching of Mathematics in 2010.
Beth Chance is Professor of Statistics at Cal Poly- San Luis Obispo and previously taught in the Department of Mathematics at the University of the Pacific. Her Ph.D. is in Operations Research, with a concentration in Statistics, from Cornell University. Her research is in evaluating the effectiveness of technology on students' learning of statistical concepts and also in assessment techniques in statistics education. She is co-author of the Workshop Statistics series and also of Investigating Statistical Concepts, Applications, and Methods, both of which adopt an active learning approach to learning introductory statistics. She is Assistant Editor of the Statistics Education Research Journal. She is a Fellow of the American Statistical Association and a recipient of the ASA's Waller Education Award and the Mu Sigma Rho Statistical Education Award.
Robert Gould, UCLA
"Citizen Statisticians: Modern Statistics for Modern Students"
Hotelling asked many important questions about the state of statistics education in his 1948 Annals paper, and in doing so, created a dichotomy between producers and consumers of statistics that still stands in today's statistics curricula. But because statistics is the science of data, our efforts would be better spent thinking of students in terms of producers and consumers of data. This is an appropriate shift of focus, because today's students bring something new to the classroom: immersive experience with data. Modern technology makes it easy for all students to produce, process, display and collect data. The dichotomy disappears, and rather than educate producers or consumers, we should instead prepare citizen statisticians: people who will interact with data in both formal and informal settings, in planned and surprising fashions, and in professional and personal contexts, throughout their lives.
About the speaker
Robert Gould received a B.S. in Applied Mathematics from Harvey Mudd College in 1987 and a Ph.D. in Mathematics from U.S. San Diego in 1994. He has been with the UCLA Statistics Department since its founding in 1998 (before which he was in the UCLA Mathematics Department) where he has served as Vice-chair of Undergraduate Studies and Director of the Center for Teaching Statistics. Statistics Education is a passion, and he has served on several ASA educational related committees and projects, including the Undergraduate Statistics Education Initiative, the Advisory Committee on Teacher Enhancement, and the GAISE writing committee. He has been a reader for AP Statistics for several years, and a co-PI with Roxy Peck on the INSPIRE program to provide statistical training for AP Stats teachers. Currently, he is chair of the ASA Section on Statistics Education and president of his local chapter of ASA. He loves gadgets of all kinds, and is currently looking for a good educational use for an iPad.
Dennis Pearl, The Ohio State University
"Personalized Education - My Thirty Year Search for the Next BIG Thing"
I will describe my thirty-year pursuit of the next BIG thing in statistics education from my days as a shipping and receiving clerk at a dried fruit packing house; to multiple redesigns of our introductory statistics class; to working on building a national infrastructure for statistics education; to my recent adventure being held up at the Canadian border for being a statistics professor. A common theme keeps re-emerging: to effectively reach all learners we must provide a way to personalize each teacher's pedagogical options and each student's educational experience. My happy conclusion is that this dream is within reach: support for statistics education has never been higher; resources have never been more abundant, and the technology required for personalization is now ubiquitous.
About the speaker
Dennis K. Pearl is a professor of Statistics and Biostatistics at The Ohio State University. He is the director of the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE), a Redesign Scholar of the National Center for Academic Transformation (NCAT), and a Fellow of the American Statistical Association. He has been the PI or Co-PI of over fifty national awards from NIH or NSF including education grants such as the CAUSEweb.org NSF NSDL digital library grant, the NSF CCLI CAUSEway national workshop program, the NSF CCLI phase I DANSER assessment infrastructure project, and the NSF CCLI phase 2 CAUSEmos outreach grant. Dr. Pearl is co-developer of the Electronic Encyclopedia of Statistical Examples and Exercises, offering several thousand pages of background, protocols, data, and questions on a variety of real-world stories. He also led the NCAT-funded team at Ohio State that developed the "buffet strategy" for teaching introductory statistics that was recognized by the ComputerWorld Honors program in 2003 as one of the top seven uses of technology in education worldwide and as a winner of the 2006 Sloan Consortium award for Effective Teaching Practice.
Wayne Stewart, University of Auckland
"Bayesian Statistics: 'The Second Coming!'"
The once commonly used Bayesian paradigm is making its way back and has the potential to re define modern statistics. The credibility, applicability and the richness of the archetype are self evident. Most of the agreed disadvantages of Bayesianism namely, priors and MCMC are two edged. The priors are a backdoor for information previously found and relevant to the study. MCMC, a mathematical tool, has the added value that functions of random variables can be easily summarized even when they are analytically intractable. Although the theory is well developed and reasonably straightforward for statisticians, the teaching of it still remains a challenge specially to undergraduate students with less mathematical knowledge and skills. How can we teach Bayesian statistics in ways that will actively facilitate the use of this incredibly powerful paradigm rather than procrastinating and watching the unexploited opportunities float by?
In this talk I will illustrate the effectiveness of Bayesian statistics and how it differs from classical statistics. I will also show some fascinating examples of the paradigm by a meaningful comparison of confidence intervals with Bayesian credible intervals to point out its interpretational simplicity and advantages. Bayesian estimates can be biased but will often have better frequentist mean squared errors. Hierarchical modeling which is easily accomplished in a Bayesian framework and difficult to perform within the classical paradigm is a natural way of pooling information to produce smaller interval estimates for parameters.
About the speaker
Wayne Stewart is an accomplished teacher with a varied background. He has taught high school mathematics and physics at public and private schools within USA and NZ. He has an MA(Hon) in ancient Greek language and is a pastor at a local church in Auckland, which is consistent with a Bayesian approach to 2Tim.2:15 where his prior is consistent with the Biblical likelihood.
Wayne obtained his PhD in Bayesian local sensitivity analysis at the University of Auckland in 2007. He teaches an undergraduate introductory Bayesian course which he developed in 2009. He is also the course coordinator of a second year Biometry paper and is part of a team teaching a classically driven data analysis statistics paper. Twice a year he gives seminars for the Certificate of Official Statistics in Wellington, NZ. His approach is lively and will often stretch the boundaries of teaching norms.
He is a member of the scientific program committee of the next DELTA conference (Teaching and Learning Undergraduate Mathematics and Statistics) to be held at Rotorua, NZ 2011. He has recently been invited to become a member of the Faculty of Science Teaching and Learning Innovation Group at the University of Auckland.
Wayne has been active in promoting the use of audience response systems in the university and uses them extensively in his lectures and seminars.
Bob delMas, University of Minnesota
"It Takes a Village: Future Directions for Statistics Education Research"
Arguably, the discipline of Statistics Education is a relatively new field that has emerged over the last three decades. While fairly young, it is a fairly broad field that has investigated effective methods for teaching statistics, students' statistical reasoning and thinking, the nature and cause of faulty statistical reasoning, non-cognitive outcomes and other factors that affect the learning of statistics. The field is witnessing the application of new technologies and the exploration of new content in the teaching of statistics. The growth of the field raises the need for statistics education researchers who are educated in a variety of areas related to conducting educational research. This talk will consider implications of the new pedagogies and content for research and the type of training needed to conduct research. Future statistics education researchers will need training in statistics, educational measurement, educational research methodologies, education and learning theory. As such, it will take a village of individuals trained in a variety of areas working together to move our understanding of statistics education forward. A collaborative model for graduate training within a supportive environment that promotes brainstorming, making mistakes, arguments, discussion, creative thinking and experimentation will be presented.
About the speaker
Bob delMas is Associate Professor of Educational Psychology at the University of Minnesota. He served as chair of the joint committee on statistical education of the ASA and the American Mathematical Association of Two-Year Colleges (AMATYC) and the chair of the ASA Section on Statistical Education. He is currently Co-Editor of the Statistics Education Research Journal, serves as an Associate Editor for the Journal of Statistics Education, and servers on the Editorial Panel of the Journal for Research in Mathematics Education, serves on the Research Advisory Board (RAB) of the Consortium for the Advancement of Undergraduate Statistics Education, participates as a mentor in the RAB Research Cluster program, and serves as an advisor to the Carnegie Foundation for the Advancement of Teaching StatWay project. His primary research interest is in the study of educational experiences that promote conceptual change and development. He has been co-PI with other leaders in statistics education (e.g., Joan Garfield, Andy Zieffler, George Cobb, Beth Chance, Allan Rossman, John Holcomb) on several projects funded by the National Science Foundation. These projects include the Assessment Resource Tools for Improving Statistical Thinking (ARTIST) assessment project, the Adapting and Implementing Innovative Materials in Statistics (AIMS) curriculum project, the Change Agents for Teaching and Learning Statistics (CATALST) curriculum project, and currently the Statistics Taught Using Resampling and Randomization (STURR) project in which he is developing GUI interfaces that allow introductory statistics students to use the power of R to carry out and visualize randomization and bootstrap approaches to data analysis.