Few Questions, Many Answers: Using Machine Learning To Assess How Students Connect Food-energy-water (few) Concepts

This article discusses the use of text classification machine learning models to assess student learning in interdisciplinary environmental education programs. The study focuses on the Food-Energy-Water (FEW) Nexus phenomena and evaluates students’ systems thinking capabilities. Results show a range of model performances and suggest that students struggle with advanced systems thinking, but show higher expertise in explaining changing water usage. This research is one of the first attempts to assess the links between foundational concepts and systems thinking ability in postsecondary education.