This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics
Education) / JSDSE (Journal of Statistics and Data Science Education) webinar, we
highlight the research article, Think-Aloud Interviews: A Tool for Exploring Student
Statistical Reasoning, in our Journal of Statistics and Data Science Education webinar
series. In the webinar, the presenters will discuss think-aloud interviews, in which
students narrate their reasoning in real time while solving problems. Think-aloud
interviews are a valuable but underused tool for statistics education research. In this
webinar, the presenters suggest possible use cases for think-alouds and summarize best
practices for designing think-aloud interview studies. They hope that their overview of
think-alouds encourages more statistics educators and researchers to begin using this
method.
Article:
https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2063209
Presented By: Alex Reinhart (Carnegie Mellon University), Ciaran Evans (Wake Forest
University), and Amanda Luby (Swarthmore college.
Alex Reinhart is an Assistant Teaching Professor of Statistics and
Data Science at Carnegie Mellon University. His work has ranged
from spatiotemporal data analysis to large-scale COVID surveys,
and he is interested in statistical pedagogy and course design.
Ciaran Evans is an Assistant Professor of Statistics at Wake
Forest University. He is interested in statistical education and
pedagogy, and enjoys collaborating with other educators on
teaching and research.
Amanda Luby is an Assistant Professor of Statistics at Swarthmore
College. She works on statistical methods for understanding
complex decision-making, and is also interested in statistics
education research and practice.
The webinar will take place on June 28th, from 4:00-4:30pm EDT.
Registration is required but is free:
https://www.causeweb.org/cause/webinar/jsdse/2022-06
We hope that you can join us for an informative discussion.
Sincerely,
Leigh Johnson (Capital University)
Moderator, CAUSE/JSDSE Webinar Series