Webinars

In this webinar, I will discuss a novel oral communication curriculum we developed and use with graduate students to help them communicate their scientific work with others. I'll use examples of how the students leverage elements of storytelling, stage presence, and improvisational skills to more effectively connect with and captivate audiences as they convey their research. We will also explore how these ideas can transfer into our own work, building a shared knowledge of how we can support students' (and our own) development of oral communication skills.

The idea of a "flipped classroom" has been integrated for two years into the introductory biostatistics course required of all Masters of Public Health (MPH) students at the University of Michigan. The course was divided into eight modules, with each module consisting of one or more video lectures and three modes of assessment: a quiz and two in-class projects. The in-class projects consisted of (1) data analysis of contemporary public health data sets using Excel and (2) review of statistical methods and results in manuscripts published recently in the American Journal of Public Health. This talk will review my experiences with the development of the course, with the implementation of the course, and student input received from anonymous end-of-semester evaluations. Registration: Please use the following form to register: https://redcap.hfhs.org/redcap/surveys/?s=4WH8JJ9KYH. The webinar link will be sent to you ahead of the session, and a link to the webinar recording will be sent to you about a week after the session.

Think-aloud interviews with students can be used to detect specific misconceptions and understand how students reason about statistical questions. Data from think-aloud interviews can then be used to develop conceptual assessments, design new teaching strategies, or suggest further experiments to learn how students think about statistics. In this webinar, we will discuss the benefits of using think-aloud interviews to develop conceptual assessments and the experience we have had using think-aloud interviews in two introductory-level statistics courses.

One of the interfaces that SAS® University Edition includes is the popular JupyterLab interface. You can use this open-source interface to generate dynamic notebooks that easily incorporate SAS® code and results into documents such as course materials and analytical reports. The ability to seamlessly interweave code, results, narrative text, and mathematical formulas all into one document provides students with practical experience in creating reports and effectively communicating results. In addition, the use of an executable document facilitates collaboration and promotes reproducible research and analyses. After a brief overview of SAS University Edition, this paper describes JupyterLab, discusses examples of using it to learn data science with SAS, and provides tips. SAS University Edition, which is available at no charge to educators and learners for academic, noncommercial use, includes SAS® Studio, Base SAS®, SAS/STAT®, and SAS/IML® software and some other analytical capabilities. Sponsored by SAS Global Academic Programs

In this talk, we will discuss why causal inference concepts align well with recommendations for introductory statistics courses and propose topics appropriate for such courses. In addition, we will highlight some resources for instructors interested in teaching causal inference, including a classroom activity we developed based on a popular dataset investigating the effects of youth smoking on lung function.

Rich visualizations of data not only helps the analyst with exploring hidden features in data, but is an essential tool in presenting and communicating results bringing the data to life. In this webinar, the presenter will show how the JMP Statistical Discovery Software is an excellent tool to use in the classroom to help students incorporate visualization into their analyses.Sponsored by JMP Academic Programs

This work introduces new assessment tools to measure learning outcomes of students in undergraduate statistics programs (e.g. majors) against the competencies recommended in the (2014) ASA Guidelines for Undergraduate Programs in Statistical Sciences. In short, these assessment tools seek to (1) measure student learning outcomes with respect to program objectives; (2) discover whether students are gaining additional relevant competencies not explicitly included in the program/major through extracurricular experiences; (3) facilitate comparisons across years and institutions to benefit continuous improvement of the program/major. This webinar presents uses and results after piloting with Senior/Capstone undergraduate statistics students shortly before graduation at four different institutions around the US.

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.

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.

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.