logo
ResearchBunny Logo
Abstract
This research uses machine learning to assess student systems thinking in the context of Food-Energy-Water (FEW) Nexus. The study tests whether machine learning can identify key concepts in student responses and explores student understanding of FEW interconnections. Across 26 models, accuracy ranged from 0.755 to 0.992. Students demonstrated better understanding of water usage changes than trade-offs. The research highlights the potential of text classification for assessing interdisciplinary learning and identifies future research priorities.
Publisher
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS
Published On
Aug 13, 2024
Authors
Emily A. Royse, Amanda D. Manzanares, Heqiao Wang, Kevin C. Haudek, Caterina Belle Azzarello, Lydia R. Hornes, Daniel L. Druckenbrod, Megan Shiroda, Sol R. Adams, Ennea Fairchild, Shirley Vincent, Steven W. Anderson, Chelsie Romulo
Tags
machine learning
student assessment
Food-Energy-Water Nexus
interdisciplinary learning
text classification

Related Publications

Explore these studies to deepen your understanding of the subject.

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects
Medicine and Health

Machine-learning algorithms for asthma, COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

A. Mcdowell, J. Kang, et al.

How to convince in a televised debate: the application of machine learning to analyze why viewers changed their winner perception during the 2021 German chancellor discussion
Political Science

How to convince in a televised debate: the application of machine learning to analyze why viewers changed their winner perception during the 2021 German chancellor discussion

F. Ettensperger, T. Waldvogel, et al.

How machine learning can help select capping layers to suppress perovskite degradation
Engineering and Technology

How machine learning can help select capping layers to suppress perovskite degradation

N. T. P. Hartono, J. Thapa, et al.

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol
Engineering and Technology

An electronic nose using a single graphene FET and machine learning for water, methanol, and ethanol

T. Hayasaka, A. Lin, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny