Lisa Green (Middle Tennessee State University)
Tuesday, June 11, 2019 - 2:00pm ET
At Middle TN State University (MTSU), the introductory statistics class is taught by a diverse set of instructors. The ideal teacher for this course would be both statistically trained and experienced in the classroom. However, we often have people who are experienced teachers, but not statistically trained, like instructors with a Master’s in mathematics. Or statistically trained, but not experienced teachers, like graduate students in our Biostatistics program.
When we decided to change the teaching method of this class to focus on more active-learning and less lecture-based classes, we had to consider the various types of instructors, and reasons they might feel uncomfortable with this change. We formed a course community in which all the instructors of this course were invited to meet approximately every two weeks during the semester before the change and the semester in which the change happened. This webinar will discuss how the course community functioned and the effects that it had on the teaching of this course.
Jung Jin Lee (Soongsil University, Korea)
Tuesday, April 9, 2019 - 4:00pm ET
eStat, www.estat.me, is a free, web-based, dynamic graphical software developed by my team which can do not only data processing as other statistical packages, but also simulation experiments for teaching statistics. The eStat covers data visualization, parametric tests, nonparametric tests, analysis of variance and regression with statistical distributions such as Binomial, Normal, t, ChiSquare, F, Wilcoxon distribution etc. An introductory statistics book for mobile teaching which utilizes QR codes of the eStat is developed and it has been used successfully for introductory statistics classes at many universities in Korea.
Beth Chance (Cal Poly San Luis Obispo) and Nathan Tintle (Dordt College)
Tuesday, March 12, 2019 - 2:00pm ET
We recently initiated the Statistical Thinking in Undergraduate Biology (STUB) network to facilitate interdisciplinary conversations between statistics and biology educators. A key focus of the network is how to better communicate across disciplines about course goals, identify synergies and create on-campus conversations with biologists teaching statistical content in their courses. In this webinar, we’ll share our experiences from the first workshops, assessment activities and curriculum development activities of the network and give some reflections on best practices, opportunities, and next steps.
Adam Childers and David Taylor, Roanoke College
Tuesday, February 12, 2019 - 2:00pm ET
Classroom Stats is an integrated mobile and web-based data collection and analysis platform. Instructors can quickly send out questions (quantitative and categorical) through the web application that students can answer on their mobile devices and see the results analyzed in real time. Classroom Stats makes teaching and learning statistics fun and interactive as it seamlessly integrates students’ data into the classroom.
Yubaihe Zhou and Dennis Pearl (Penn State University)
Wednesday, January 9, 2019 - 2:00pm ET
Each summer ten Penn State undergraduate statistics majors develop R Shiny apps for teaching and then field-test them in courses the following academic year. This webinar will describe this summer research program and its benefits for the students involved, and also showcase the apps produced for both introductory and upper division statistics courses (they are available at https://shinyapps.science.psu.edu/).
Philipp Burckhardt, Francis R. Kovacs, Rebecca Nugent, and Ron Yurko
Tuesday, December 11, 2018 - 2:00pm ET
In an effort to respond to the growing need to support active engagement with the entire data analysis pipeline at the introductory level, Carnegie Mellon Statistics & Data Science is building ISLE (Integrated Statistics Learning Environment), an interactive, e-learning platform that removes the computing cognitive load and lets students explore Statistics & Data Science concepts in structured and unstructured ways. Usable both inside and outside of the classroom, the browser-based platform also supports student-driven inquiry and case studies. The platform is flexible enough to allow adaptation, providing different modes of data analysis instruction, active learning opportunities, group work, and exercises for different subsets of the population. Students are also able to build their own case studies with little restriction or faculty intervention. In an effort to characterize different student approaches to data analysis, we track and model every click, word used, and decision made throughout the data analysis pipeline from loading the data to the final written report. These metrics can be displayed to instructors, some in real-time and some in report format. In this demonstration, we will give an overview of ISLE’s capabilities and show some insightful examples of modeling student behavior (changing over time) with a particular focus on how students write about data. Webinar participants will be able to interact with an ISLE Data Explorer during the talk.
Jennifer Broatch (Arizona State University)
Tuesday, November 13, 2018 - 2:00pm ET
Course Based Undergraduate Research Experiences (CUREs) are rapidly becoming a model for undergraduate science education in which interdisciplinary students enroll in a course that is focused on a research question and students themselves generate hypotheses, develop protocols, generate the data, analyze and present the outcome. Hence, the experimental design and statistical analysis of the student developed research questions is critical. This presentation will include the experimental design process activities and handouts that guide the students from a variety of backgrounds through all phases of the experiment: Pre-planning, experimental design and analysis. A discussion of the implementation of multiple CUREs will also be discussed.
John Holcomb (Cleveland State University)
Tuesday, October 9, 2018 - 2:00pm ET
At Cleveland State University, with funding from NSF, we have adopted a supplemental instruction model for all precalculus courses and select sections of calculus. In this approach, the supplemental instruction is mandatory and led by upperclassman that we call SPTs (STEM Peer Teachers). In this webinar I will discuss the model, the result of higher pass rates in these classes and how we have begun adapting this approach in statistics I & II classes.
Ryne VanKrevelen, Lisa Rosenberg, and Laura Taylor (Elon University)
Tuesday, August 21, 2018 - 2:00pm ET
The Islands is a virtual world, created by Dr. Michael Bulmer from the University of Queensland, that can be used as a vehicle for student-led data collection. The Islands allows students to encounter “real-world” issues like obtaining consent, respondents who don’t tell the truth, measurement variability, and more in a safe environment. We have begun the early stages of investigating how student enjoyment, confidence, and learning differ between projects that use The Islands versus those that have students collect their own “real-world” data. In this webinar, we will introduce several features of The Islands, explain how we have used it in our introductory statistics classes, and share initial results from our research comparing these two types of projects.
Nicholas J. Horton (Amherst College)
Tuesday, July 10, 2018 - 2:00pm ET
As our economy, society, and daily life become increasingly dependent on data, work across nearly all fields is becoming more data driven, affecting both the jobs that are available and the skills that are required. At the request of the National Science Foundation, the National Academies of Sciences, Engineering, and Medicine were asked to set forth a vision for the emerging discipline of data science at the undergraduate level. The study committeem considered the core principles and skills undergraduates should learn and discussed the pedagogical issues that must be addressed to build effective data science education programs. The report underscores the importance of preparing undergraduates for a data-enabled world and recommends that academic institutions and other stakeholders take steps to meet the evolving data science needs of students. In this webinar, implications, opportunities, and challenges for statistics educators will be discussed along with the study findings.