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

Education

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

E. A. Royse, A. D. Manzanares, et al.

Unlock the potential of machine learning in education! This innovative research examined how machine learning can assess students' understanding of the complex Food-Energy-Water Nexus, revealing impressive accuracy in identifying key concepts in their responses. Conducted by a diverse group of scholars, the findings highlight the strengths of students' knowledge about water usage but also unveil challenges in grasping trade-offs.

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Playback language: English
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
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