INVESTIGATING UNDERGRADUATE STUDENTS’ REASONING ABOUT SOCIO-HYDROLOGICAL ISSUES: RESULTS FROM A TRANSDISCIPLINARY WATER COURSE

Societies today face an array of global, water-related challenges with significant scientific dimensions within the Food-Energy-Water-Nexus. To prepare students to become tomorrow’s global citizens, postsecondary learning experiences must provide them with the ability to learn and reason about socio-hydrological issues such as agricultural water use, water quality, and water security. However, prior research has illustrated limitations in undergraduate students’ disciplinary knowledge and little research has been conducted to understand how they use this knowledge to solve problems and make decisions about socio-hydrological systems (i.e., water literacy). Here, we report on discipline-based education research from an innovative, interdisciplinary course, Water in Society, in which we – a team of faculty and graduate students with expertise in hydrology, economics, and science education - engage a diverse population of students – both STEM and non-STEM majors – from a variety of backgrounds. Principles of effective undergraduate STEM instruction underlying the course include and emphasis on active learning, socio-hydrological systems, student engagement with authentic hydrological data, and computer-based modeling tools. We investigate undergraduate students’ model-based reasoning about socio-hydrological systems. Findings provide evidence for growth in students’ conceptual understanding of water-related phenomena and model-based reasoning about socio-hydrological systems over the course of the semester. They also provide insight into how students leverage modeling tools grounded in authentic hydrologic datasets to problem-solve real-world, water-related challenges. Gain scores for pre-/post-course assessments of students’ content knowledge were predictive of their socio-hydrological reasoning. However, relationships between students’ science content knowledge and model-based reasoning about water systems differed significantly across two course projects involving computer-based models. We use these empirical findings to consider both challenges and opportunities in course design, pedagogy, and assessment that optimize student-learning experiences. Results presented here build upon previous findings from earlier iterations of the course.