A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills
| dc.contributor.author | Lofton, Mary E. | en |
| dc.contributor.author | Moore, Tadhg N. | en |
| dc.contributor.author | Woelmer, Whitney M. | en |
| dc.contributor.author | Thomas, R. Quinn | en |
| dc.contributor.author | Carey, Cayelan C. | en |
| dc.date.accessioned | 2024-10-21T19:37:31Z | en |
| dc.date.available | 2024-10-21T19:37:31Z | en |
| dc.date.issued | 2024-10-10 | en |
| dc.description.abstract | Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates. | en |
| dc.description.version | Accepted version | en |
| dc.format.extent | 12 page(s) | en |
| dc.format.mimetype | application/vnd.openxmlformats-officedocument.wordprocessingml.document | en |
| dc.identifier.doi | https://doi.org/10.1093/biosci/biae089 | en |
| dc.identifier.eissn | 1525-3244 | en |
| dc.identifier.issn | 0006-3568 | en |
| dc.identifier.orcid | Thomas, Robert [0000-0003-1282-7825] | en |
| dc.identifier.orcid | Carey, Cayelan [0000-0001-8835-4476] | en |
| dc.identifier.uri | https://hdl.handle.net/10919/121359 | en |
| dc.language.iso | en | en |
| dc.publisher | Oxford University Press | en |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en |
| dc.subject | ecosystem modeling | en |
| dc.subject | National Ecological Observatory Network (NEON) | en |
| dc.subject | student engagement | en |
| dc.subject | training program | en |
| dc.subject | undergraduate education | en |
| dc.title | A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills | en |
| dc.title.serial | BioScience | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |
| dc.type.other | Article | en |
| dc.type.other | Early Access | en |
| dc.type.other | Journal | en |
| pubs.organisational-group | Virginia Tech | en |
| pubs.organisational-group | Virginia Tech/Science | en |
| pubs.organisational-group | Virginia Tech/Science/Biological Sciences | en |
| pubs.organisational-group | Virginia Tech/All T&R Faculty | en |
| pubs.organisational-group | Virginia Tech/Science/COS T&R Faculty | en |
| pubs.organisational-group | Virginia Tech/Post-docs | en |