FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts

Emily A. Royse, Amanda D. Manzanares, Heqiao Wang, Kevin C. Haudek, Caterina Belle Azzarello, Lydia R. Horne, Daniel L. Druckenbrod, Megan Shiroda, Sol R. Adams, Ennea Fairchild, Shirley Vincent, Steven W. Anderson and Chelsie Romulo ()
Additional contact information
Emily A. Royse: Aims Community College
Amanda D. Manzanares: School of Psychological Sciences at the University of Northern Colorado
Heqiao Wang: Educational Psychology and Special Education at Michigan State University
Kevin C. Haudek: Department of Biochemistry and Molecular Biology at Michigan State University
Caterina Belle Azzarello: School of Psychological Sciences at the University of Northern Colorado
Lydia R. Horne: Unity Environmental University
Daniel L. Druckenbrod: Department of Earth and Chemical Sciences at Rider University
Megan Shiroda: Department of Human Biology at Michigan State University
Sol R. Adams: Metropolitan State University of Denver
Ennea Fairchild: Pacific Northwest National Laboratory
Shirley Vincent: Vincent Evaluation Consulting LLC
Steven W. Anderson: University of Northern Colorado
Chelsie Romulo: Department of Geography, GIS and Sustainability at University of Northern Colorado

Palgrave Communications, 2024, vol. 11, issue 1, 1-18

Abstract: Abstract There is growing support and interest in postsecondary interdisciplinary environmental education, which integrates concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinking capabilities using two questions anchored by the Food-Energy-Water (FEW) Nexus phenomena by answering two questions (1) Can machine learning models be used to identify instructor-determined important concepts in student responses? (2) What do college students know about the interconnections between food, energy, and water, and how have students assimilated systems thinking into their constructed responses about FEW? Reported here is a broad range of model performances across 26 text classification models associated with two different assessment items, with model accuracy ranging from 0.755 to 0.992. Expert-like responses were infrequent in our dataset compared to responses providing simpler, incomplete explanations of the systems presented in the question. For those students moving from describing individual effects to multiple effects, their reasoning about the mechanism behind the system indicates advanced systems thinking ability. Specifically, students exhibit higher expertise in explaining changing water usage than discussing trade-offs for such changing usage. This research represents one of the first attempts to assess the links between foundational, discipline-specific concepts and systems thinking ability. These text classification approaches to scoring student FEW Nexus Constructed Responses (CR) indicate how these approaches can be used, in addition to several future research priorities for interdisciplinary, practice-based education research. Development of further complex question items using machine learning would allow evaluation of the relationship between foundational concept understanding and integration of those concepts as well as a more nuanced understanding of student comprehension of complex interdisciplinary concepts.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41599-024-03499-z Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03499-z

Ordering information: This journal article can be ordered from
https://www.nature.com/palcomms/about

DOI: 10.1057/s41599-024-03499-z

Access Statistics for this article

More articles in Palgrave Communications from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().