Resource Image

STEM Inclusive Teaching Practices Webinar Series: Socially Relevant Teaching in the Time of COVID-19

Author(s): Joanna Wares1, Marcella Torres1, zeynep teymuroglu2, Grace Stadnyk3, Casey Hawthorne3, E Cabral Balreira4

1. University of Richmond 2. Rollins College 3. Furman University 4. Trinity University

5054 total view(s), 719 download(s)

0 comment(s) (Post a comment)

Summary:
Presents various mathematical modeling and data science activities created around analyzing and interpreting COVID-19 data. Much of the instructional guidance provided for the activities and projects is easily adaptable to a remote learning…

more

Presents various mathematical modeling and data science activities created around analyzing and interpreting COVID-19 data. Much of the instructional guidance provided for the activities and projects is easily adaptable to a remote learning environment.

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 18 Feb 2021 doi:10.25334/D7QV-9710 - cite this

Description

EDSIN (Environmental Data Science Inclusion Network), B(ui)LDS (Biological Universal and Inclusive Learning in Data Science, BLUE (Biodiversity Literacy in Undergraduate Education), iDigBio (Integrated Digitized Biocollections), and QUBES (Quantitative Undergraduate Biology Education and Synthesis) are organizing a new webinar series entitled “Inclusive Teaching Practices in STEM Education.” The purpose of this series is to initiate discussion on topics related to inclusive teaching practices while building community among a diversity of STEM disciplines interested in creating a more inclusive learning environments for undergraduate students. Our partners represent very different communities in the world of STEM, but we are all really interested in fostering more diverse and inclusive communities, so one goal of this project is to raise awareness of the existing knowledge base and resources that exist.

Title: Socially Relevant Teaching in the Time of COVID-19

Date: February 4th, 2021 at 3:00pm ET

Abstract: We present various mathematical modeling and data science activities created around analyzing and interpreting COVID-19 data. Much of the instructional guidance provided for the activities and projects is easily adaptable to a remote learning environment. Additionally, these activities and projects address complex social issues related to COVID-19 such as inequality in testing, wealth distributions, or race/ethnicity issues. Preliminary data suggest that COVID-19 disproportionately impacts minorities and low-income households. Some of the modules focus on using mathematics to better understand these inequities, which can help facilitate rich discussions. In addition, we share some of our experiences in teaching these activities across the curriculum.

Panelists: 

  • Joanna Wares, University of Richmond 
  • Marcella Torres, University of Richmond 
  • Zeynep Teymuroglu, Rollins College 
  • Grace Stadnyk, Furman University
  • Casey Hawthorne, Furman University

Cite this work

Researchers should cite this work as follows: