Natural Resources, School of
School of Natural Resources: Faculty Publications
ORCID IDs
Dauer http://orcid.org/0000-0002-8971-0441
Couch http://orcid.org/0000-0001-6972-6561
Mayes http://orcid.org/0000-0003-2726-2409
Rittschof http://orcid.org/0000-0003-3213-7266
Document Type
Dataset
Date of this Version
2025
Citation
Dataset, 2025
dataset doi: 10.32873/unl.dc.oth.027
Abstract
Quantitative modelling is a fundamental skill for biology students with performance determined by a host of cognitive and metacognitive resources and requiring instructors to integrate key quantitative dimensions into teaching biology. Using an assessment of quantitative modelling in biology, we explored biology students’ (n = 930 higher education students) modelling practices (i.e., abilities to create, test, revise), metacognition about the nature of models and confidence in their capabilities to model. Students performed best on items related to understanding variables used in models and performed more poorly on items related to interpreting, creating, and using models to understand biological phenomena. Students possessed a high level of metacognition about the nature and purpose of models for science and were highly confident, even overconfident, in their abilities to use, create, and interpret models in science. Students’ metacognition of models and confidence were significantly correlated with performance on modelling practices, suggesting that knowing the nature of models in science and confidence in one’s abilities can relate to model use. Improving the knowledge, skills, and abilities of biology students to perform quantitative interpretations and quantitative modelling and supporting their metacognition about and confidence in their abilities may be a crucial step toward better preparing students for careers in biology fields.
Dauer et al 2025 DATASET QM-BUGS
Included in
Biology Commons, Cognition and Perception Commons, Educational Psychology Commons, Higher Education Commons, Natural Resources and Conservation Commons, Natural Resources Management and Policy Commons, Science and Mathematics Education Commons