Abstract
AbstractEcologists 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.
Highlights
Ecological research is becoming more dataintensive, as many ecologists commonly acquire, manage, and analyze large volumes of quantitative and qualitative information (Michener and Jones 2012, Hampton et al 2013, Schimel and Keller 2015)
We developed three EDDIE modules for use in freshwater ecology courses to better prepare undergraduate students to participate in the use of long-term and high-frequency data
This study addresses whether the completion of the suite of modules improves students’ quantitative literacy and understanding of ecological concepts
Summary
Ecological research is becoming more dataintensive, as many ecologists commonly acquire, manage, and analyze large volumes of quantitative and qualitative information (Michener and Jones 2012, Hampton et al 2013, Schimel and Keller 2015). These data span both long periods of time (>1 decade) and high measurement frequencies. Ecologists are analyzing large datasets containing high-frequency data collected by automated sensors. Innovations in sensor technology and data analysis and increased data sharing are rapidly increasing the availability of highfrequency datasets for ecologists (Michener et al 2011, Reichman et al 2011, Weathers et al 2013).
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