Project EDDIE > EDDIE Earth and Ecosystems > About this Project

About the EDDIE: Earth and Ecosystems Project


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The emergence of large, sensor-based datasets provides an opportunity to engage students in STEM and improve quantitative reasoning through open-ended exploration and interpretation of real-world data. EDDIE Earth and Ecosystems is a collaboration among STEM disciplinary and educational researchers. We aim to develop flexible classroom modules using large, publicly available, digital data for undergraduate students in biology, geology, and environmental science, as well as provide the associated professional development needed to ensure their effective use. Our previous NSF TUES pilot award allowed us to demonstrate that our modules can be highly effective (Carey and Gougis, 2017; Klug et al., 2017; O'Reilly et al., 2017; Soule et al., in press).

The wealth of large authentic datasets online provides an opportunity to engage students in scientific inquiry while simultaneously improving quantitative reasoning. Open-ended exploration through the analysis and interpretation of large datasets can have substantial benefits as students explore the stochastic nature of environmental and Earth systems (Brewer and Gross, 2003; Ellwein et al., 2014; Gougis et al., 2016). Using authentic and publicly-accessible online datasets to address real-world questions reinforces the need and rationale for developing quantitative reasoning skills, leading to an increased appreciation for large complex datasets associated with basic environmental monitoring (Ellwein et al., 2014; O'Reilly et al., 2017). In addition, spatially resolved datasets allow students to find place-based data that are meaningful to them, and real-time data allow students to see immediate relevance.

Many of these large datasets are based on high-frequency sensor systems, and increasing their availability provides unique opportunities to develop quantitative reasoning skills, particularly those associated with visualizing, analyzing, and interpreting quantitative data. Quantitative reasoning (QR) refers to the ability to interpret data and to reason with numbers in real-world situations (Steen, 2004), and working with large authentic datasets can provide the contextualization needed for meaningful student engagement. Graphing is a key element of QR and scientific literacy because data visualization is a critical step for initial exploration, for fostering scientific knowledge, and for effective communication of complex information (AAAS, 2011). Undergraduate students' ability to comprehend and conceptualize data need improvement and summarizing, condensing, displaying, interpreting, and communicating quantitative data remains a persistent challenge in science

In Project EDDIE we will develop curricular material that improves quantitative reasoning and to develop a self-sustaining community of practice (sensu Lave and Wegner, 1991). We will focus on topics related to the environmental and Earth sciences, allowing the greatest potential for adoption across a wide range of STEM courses, from physical to life sciences.

Project Goals

We are developing curricular material that improves quantitative reasoning and to develop a self-sustaining community of practice (sensu Lave and Wegner, 1991). We will focus on topics related to the environmental and Earth sciences, allowing the greatest potential for adoption across a wide range of STEM courses, from physical to life sciences. Our goals are to:

  1. Develop a suite of at least 30 flexible modular curricular materials, using large publicly available online datasets, that contribute to improved student quantitative reasoning.
  2. Develop a community of faculty members engaged with materials and professional development designed to foster pedagogical orientation favoring open inquiry with large datasets.
  3. Determine what mechanisms contribute to shifts in instructors' pedagogical orientation towards inquiry-based teaching.

Research, Evaluation, and Assessment

The Eddie project incorporates education research, internal and external program evaluation, and assessment to determine the success of the project and to generate new knowledge on how to structure community and individual interventions to improve teaching and learning of quantitative reasoning.

Research, evaluation, and assessment activities will help the project understand and align with community needs, inform the workshop structure, investigate student learning gains, and examine the efficacy of the project design. Incorporating the research and evaluation team as part of the project leadership enables the project to be nimble and responsive to findings throughout the project, resulting in strong and relevant materials.

The research and evaluation focuses on understanding: community needs; student learning gains; growth, reach, and impacts of the community; the influence of faculty pedagogical orientations; the influence of professional development; and overall project success.

References

AAAS, 2011. Vision and Change in Undergraduate Biology Education: A Call to Action. AAAS Washington, DC.

Brewer, C.A., and L.J. Gross, 2003. Training ecologists to think with uncertainty. Ecology 84(6):1412:1414. DOI: 10.1890/0012-9658(2003)084[1412:TETTWU]2.0.CO;2.

Carey, C.C., and R.D. Gougis, 2017. Simulation modeling of lakes in undergraduate and graduate classrooms increases comprehension of climate change concepts and interest in computational tools. Journal of Science Education and Technology. DOI:10.1007/s10956-016-9644-2.

Ellwein, A.L, L.M. Hartley, S. Donovan, and I. Billick, 2014. Using rich context and data exploration to improve engagement with climate data: Bringing a field station into the college classroom. Journal of Geoscience Education 62:578-586. DOI: 10.5408/13-034.

Gougis, R.D., J. F. Stomber, A. O'Hare, N. E. Bader, T. Meixner, C.M. O'Reilly, and C.C Carey, 2016. If random is to have no pattern, how can we predict variation in a random sample?: Post-secondary science students' concepts of randomness and variation. International Journal of Mathematics and Science Education. DOI10.1007/s10763-016-9737-7.

Klug, J.L., C.C. Carey, D.C. Richardson, and R.D. Gougis, 2017. Integrating high-frequency and long-term data analyses into undergraduate ecology classes improves quantitative literacy. Ecosphere 8(3): e01733. DOI: 10.1002/ecs2.1733.

Lave, J., and E. Wenger, 1991. Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.

O'Reilly, C.M., R. Darner Gougis, J.L. Klug, C.C. Carey, D.C. Richardson, N.E. Bader, D. Soule, D. Castendyk, T. Meixner, J.F. Stomberg, K.C. Weathers, and W. Hunter, 2017. Using large datasets for open-ended inquiry in undergraduate classrooms. Bioscience. 67(12): 1052-1061.

Soule, D., R. Gougis, C.M. O'Reilly, N.E. Bader, T. Meixner, C.A. Gibson, and R.E. McDuff. In press. EDDIE modules are effective learning tools for developing quantitative literacy and seismological understanding. Journal of Geoscience Education.

Steen, L.A., 2004. Everything I needed to know about averages I learned in college. Peer Review 6(4): 4-8.


Project Partners







Project Support

Project EDDIE is supported by funding from NSF (Earth and Ecosystems IUSE Award 1821567; Environmental Data DEB Award: 1245707; and MacrosystemsEF Award: 1702506) and ACI (Award: 1234983)