ENVIRONMENTAL DATA-DRIVEN INQUIRY AND EXPLORATION (PROJECT EDDIE): MODULES THAT ENGAGE STUDENTS IN QUANTITATIVE REASONING AND SCIENTIFIC DISCOURSE USING LARGE, HIGH-FREQUENCY AND SENSOR-BASED DATASETS

Scientists are increasingly using sensor-collected, high-frequency and long-term datasets to study geological and environmental processes. To expose undergraduate students to such real-world experiences, our team developed classroom modules that utilize large, long-term, high-frequency and sensor-based datasets for undergraduate and graduate science courses. Our interdisciplinary team of faculty and research scientists developed flexible modules suitable for a variety of introductory, mid-level, and advanced courses that meet a series of pedagogical goals, allowing students to: (i) manipulate large datasets to conduct real-world, inquiry-based investigations; (ii) develop reasoning about statistical variation; and (iii) become excited about first-hand experiences with the scientific process. To date, we have developed ten modules on the following subjects: (1) lake ice-off phenology; (2) lake metabolism; (3) lake mixing; (4) lake modeling; (5) stream discharge; (6) water quality; (7) nutrient loading; (8) climate change; (9) soil respiration; and (10) seismology. Each module requires students to collect data from online sources, such as discharge and water quality data from the US Geological Survey, ecosystem carbon dioxide flux data from FLUXNET, lake temperature data from the Global Lake Ecological Observatory Network, and seismic data from the Incorporated Research Institutions for Seismology. To assess achievement of the pedagogical goals during the 2014-15 and 2015-16 academic years, we used pre- and post-module student questionnaires. This information allowed us to determine whether our modules were effective at engaging students and increasing their quantitative skills, and to revise modules prior to widespread online dissemination in 2016. Our initial results suggest that students appreciate the value of high-resolution and long-term data, and that working with large datasets cements the real-world application of basic geological and environmental concepts. This project is funded by a National Science Foundation TUES grant (Transforming Undergraduate Education in STEM; NSF DEB 1245707). Completed modules and additional information are available on the project web page: www.projecteddie.org/