AboutMembershipPublicationsConferencesReportsNewsWebinarsLinks
IASE IASE Login

Raising statistical modeling accessibility to young learners

Presented at: 11 April 2022; 18:00 UTC

Webinar duration: 60 minutes

Presenter(s): Michal Dvir - Israel

Current and recent global events have reified the importance of empowering the public with data literacy skills, as well as the appreciation of variation, its multiple sources, and of the uncertainty that is omnipresent in our everyday lives. This calls to introduce novices to the culture of statistics – including the dispositions and critical ways of thinking that characterize this culture. Many have suggested that engaging in authentic data and statistical practices can serve as gateways to this disciplinary culture. However, it is unclear what actual procedures and techniques the learners of today would actually need as the citizens of tomorrow, because of frequent innovations in the Data Sciences. Furthermore, data and statistical practices are often inaccessible due to the complex mathematics (and probability) they require. The latter is particularly true for young (elementary school) students. Successfully raising the accessibility of some of the formal practices to young students can greatly inform how to similarly raise the accessibility of the practice to other novices.

This virtual seminar will focus on a core statistical practice, statistical modeling, whose pedagogical potential has been the subject of increased interest in statistics education research. Inspired by how the informal adaptation of the practice of formulating a statistical inference (Informal Statistical Inference, ISI) has raised its accessibility, we suggest a corollary: the practice of constructing, evaluating and refining Informal Statistical Models (ISMs). As a proof of concept, we will also examine together several examples of how young learners can construct and reason with ISMs, as they engage in a unique activity sequence. We will then discuss the potential generalizability of these examples, and how they can inform future research, teaching and curriculum design, that will further raise the accessibility of statistical modeling.

Bio

Dr. Michal Dvir is a postdoctoral fellow in the Education in Science and Technology Faculty at the Technion Israel Institute of Technology, under the guidance of Dr. Dina Tsybulsky. Her PhD dissertation focused on young students’ reasoning with Informal Statistical Models and modeling, under the supervision of Prof. Dani Ben-Zvi, in the Mathematics Education Department at the University of Haifa. Her background is in abstract mathematics. Her thesis on young learners’ statistical reasoning received the University of Haifa Rector award. Based on her achievements in her doctorate research, she was part of the prestigious Azrieli Fellowship Program promoting academic excellence and leadership.

Links

Video of webinar

Presentation slides