TITLE : Embedding communication concepts in forecasting training
increases students’ understanding of ecological
uncertainty
Submitted as an Article to Ecosphere , Eco-Education Track
AUTHOR LIST: Whitney M. Woelmera*, Tadhg N.
Moorea,b11Present address: School of
Biological, Earth and Environmental Sciences, University College Cork,
Cork, Ireland, Mary E. Loftona, R. Quinn
Thomasa,b, and Cayelan C. Careya
aDepartment of Biological Sciences, Virginia Tech,
Blacksburg, VA, USAbDepartment of Forest Resources and Environmental
Conservation, Virginia Tech, Blacksburg, VA, USA
OPEN
RESEARCH STATEMENT : This study collected and analyzed human subject
data and was approved by the Virginia Tech Institutional Review Board
(19-669) and the Carleton College Institutional Review Board (19-20
065). Data for this study have been anonymized and aggregated and can be
found at Woelmer (2023) along with all code to reproduce the analysis
and figures within this study.
Woelmer, W. 2023. Wwoelmer/module8_public_ecosphere: Ecosphere
submission March 2023 (v1.0). Zenodo.
https://doi.org/10.5281/zenodo.7733965
KEYWORDS : active learning, ecology education, ecological forecast,
Macrosystems EDDIE, R Shiny, teaching modules, translational ecology,
undergraduate curricula, visualization literacy
ABSTRACT :
Communicating and interpreting uncertainty in ecological model
predictions is notoriously challenging, motivating the need for new
educational tools which introduce ecology students to core concepts in
uncertainty communication. Ecological forecasting, an emerging approach
to estimate future states of ecological systems with uncertainty,
provides a relevant and engaging framework for introducing uncertainty
communication to undergraduate students, as forecasts can be used as
decision support tools for addressing real-world ecological problems and
are inherently uncertain. To provide critical training on uncertainty
communication and introduce undergraduate students to the use of
ecological forecasts for guiding decision-making, we developed a
hands-on teaching module within the Macrosystems EDDIE (Environmental
Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational
program. Our module used an active learning approach by embedding
forecasting activities in an R Shiny application to engage introductory
students in data science, ecological modeling, and forecasting without
needing advanced computational or programming skills. Pre- and
post-module assessment data from >250 undergraduate ecology
students indicate that the module significantly increased students’
ability to interpret forecast visualizations with uncertainty, identify
different ways to communicate forecast uncertainty for diverse users,
and correctly define ecological forecasting terms. Specifically,
students were more likely to describe visual, numeric, and probabilistic
methods of uncertainty communication following module completion.
Students were also able to identify more benefits of ecological
forecasting following module completion, with the key benefits of using
forecasts for prediction and decision-making most commonly described.
These results show promise for introducing ecological model uncertainty,
data visualizations, and forecasting into undergraduate ecology
curricula via software-based learning, which can increase students’
ability to engage and understand complex ecological concepts.