Karsten Lübke (FOM University)
Tuesday, June 9, 2020 - 2:00pm
We are living in a world full of multivariate observational data. Qualitative assumptions about the data generating process, operationalised in simple directed acyclic graph can help students to understand multivariate phenomena like Simpson's or Berkson's paradox, confounding and bias. By teaching causal inference the introductory course can overcome the mantra "correlation does not imply causation".
The webinar discusses some motivation as well as teaching ideas and the integration in the curriculum.
Jennifer Green (Montana State University)
Tuesday, February 11, 2020 - 2:00pm
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.
Mikaela Meyer & Ciaran Evans (Carnegie Mellon University)
Tuesday, December 10, 2019 - 2:00pm
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.
Kevin Cummiskey & Bryan Adams (West Point)
Tuesday, October 8, 2019 - 4:00pm
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.
Matt Beckman (Pennsylvania State University)
Tuesday, September 10, 2019 - 2:00pm
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.
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.
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.