Victoria Woodard (Notre Dame University)
Tuesday, August 20, 2019 - 2:00pm
In this webinar, I will discuss findings from a qualitative study that was conducted based on written work and task-based interviews of students completing a second course in statistics. In particular, I will focus on three major topics:
The methodology used for analyzing our qualitative data,
Beginning to define the relationship that was observed between a student’s ability to think statistically while utilizing statistical computing tools and
Observations about how students solve problems while utilizing statistical computing tools.
Hollylynne Lee (NC State University)
Tuesday, July 9, 2019 - 2:00pm
As statistics and data science become more important and prominent in secondary schools, we need more teachers ready to teach statistics in data-rich ways. Enhancing Statistics Teacher Education through E-Modules [ESTEEM] is an NSF-funded project to develop and disseminate research-based online learning materials to be used in teacher education courses (http://hirise.fi.ncsu.edu/projects/esteem). In this webinar, participants will be introduced to our online materials, including videos of students and teachers engaged in rich statistics tasks, interviews with experts educators, and investigations with a free online tool CODAP. Different implementation models used and evaluation results will be shared. Participants will learn how to register for free access to materials and download all materials in common Learning Management System formats (Moodle, Canvas, Blackboard) that are ready for upload into their own courses.
Lisa Green (Middle Tennessee State University)
Tuesday, June 11, 2019 - 2:00pm
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.
Adam Sullivan (Brown University)
Thursday, May 30, 2019 - 2:00pm
Flipped classrooms have appeared in all levels of education. One of the major benefits is that the passive learning (lecture) is completed at home and the active learning (activities and problem solving) are done in class with the instructor. However, the issues with flipped classrooms are the cost to make high quality video content and the time. Due to the cost and time many classes are created and then not updated. This talk will discuss common ways for creating and updating flipped classrooms, considering a case study of PHP 2560: Statistical Programming in R at Brown University. We will discuss the first flipped version of this course, in terms of content and creation time. Then we will discuss how subsequent iterations have been adapted and updated to maintain relevance.
Jung Jin Lee (Soongsil University, Korea)
Tuesday, April 9, 2019 - 4:00pm
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
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
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
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
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
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.