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Analysis of high-frequency and long-term data in undergraduate ecology classes improves quantitative literacy

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dc.contributor.authorKlug, Jennifer L.en
dc.contributor.authorCarey, Cayelan C.en
dc.contributor.authorRichardson, David C.en
dc.contributor.authorGougis, Rebekka Darneren
dc.contributor.departmentBiological Sciencesen
dc.date.accessioned2019-05-08T17:52:13Zen
dc.date.available2019-05-08T17:52:13Zen
dc.date.issued2017-03en
dc.description.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.en
dc.description.notesWe thank the students who participated in this study, Randy Fuller for contributing to the initial development of the Lake Mixing module, Janet Stromberg for help with qualitative data analysis, and Jon Doubek and Kate Hamre for teaching assistance. We also thank our Project EDDIE colleagues, Nick Bader, Devin Castendyk, Randy Fuller, Cathy Gibson, Luke Nave, Catherine O'Reilly, Dax Soule, Tom Meixner,and Kathleen Weathers, for helpful discussions. Project EDDIE is funded by NSF TUES 1245707, with administrative support from CeMaST (Center for Mathematics, Science and Technology at Illinois State University).en
dc.description.sponsorshipProject EDDIE-NSF TUES [1245707]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/ecs2.1733en
dc.identifier.eissn2150-8925en
dc.identifier.issue3en
dc.identifier.othere01733en
dc.identifier.urihttp://hdl.handle.net/10919/89386en
dc.identifier.volume8en
dc.language.isoenen
dc.publisherEcological Society of Americaen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectfreshwater ecologyen
dc.subjectGlobal Lake Ecological Observatory Networken
dc.subjectice phenologyen
dc.subjectlake metabolismen
dc.subjectlake stratificationen
dc.subjectProject Environmental Data-Driven Inquiry and Explorationen
dc.subjectquantitative skillsen
dc.subjectteaching modules.en
dc.titleAnalysis of high-frequency and long-term data in undergraduate ecology classes improves quantitative literacyen
dc.title.serialEcosphereen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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