Analysis and synthesis of large and complex datasets are increasingly important components of scientific research. To expose undergraduate students to these datasets and to develop valuable data analysis skills, a team of environmental scientists and education researchers created Project EDDIE (Environmental Data-Driven Inquiry and Exploration). Project EDDIE is a pedagogical collaborative that develops and assesses flexible modules that use publicly-available, large datasets that allow students to explore a range of concepts in the biological, earth, and environmental sciences. Modules have been implemented in a range of courses, class sizes, and institutions. We assessed six modules over eight courses, which were taught to total of 1,380 students. EDDIE modules led to significant improvements in students' competence using spreadsheet software and as well as their conceptual understanding of how to use large complex datasets to address scientific problems. Furthermore, students reported positive and informative experiences using large datasets to explore open-ended questions.

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Ecosystem modeling is a critically important tool for environmental scientists, yet is rarely taught in undergraduate and graduate classrooms. To address this gap, we developed a teaching module that exposes students to a suite of modeling skills and tools (including computer programming, numerical simulation modeling, and distributed computing) that students apply to study how lakes around the globe are experiencing the effects of climate change. In the module, students develop hypotheses about the effects of different climate scenarios on lakes and then test their hypotheses using hundreds of model simulations. We taught the module in a 4-hour workshop and found that participation in the module significantly increased both undergraduate and graduate students' understanding about climate change effects on lakes. Moreover, participation in the module also significantly increased students' perceived experience level in using different software, technologies, and modeling tools. By embedding modeling in an environmental science context, non-computer science students were able to successfully use and master technologies that they had previously never been exposed to. Overall, our findings suggest that modeling is a powerful tool for catalyzing student learning on the effects of climate change.

Abstract. Ecologists are increasingly analyzing long-term and high-frequency sensor datasets as part of their research. As ecology becomes a more data-rich scientific discipline, the next generation of ecologists needs to develop the quantitative literacy required to effectively analyze, visualize, and interpret large datasets. We developed and assessed three modules to teach undergraduate freshwater ecology students both scientific concepts and quantitative skills needed to work with large datasets. These modules covered key ecological topics of phenology, physical mixing, and the balance between primary production and respiration, using lakes as model systems with high-frequency or long-term data. Our assessment demonstrated that participating in these modules significantly increased student comfort using spreadsheet software and their self-reported competence in performing a variety of quantitative tasks. Interestingly, students with the lowest pre-module comfort and skills achieved the biggest gains. Furthermore, students reported that participating in the modules helped them better understand the concepts presented and that they appreciated practicing quantitative skills. Our approach demonstrates that working with large datasets in ecology classrooms helps undergraduate students develop the skills and knowledge needed to help solve complex ecological problems and be more prepared for a data-intensive future.

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