ED31B-0902
Project EDDIE: Improving Big Data skills in the classroom
Wednesday, 16 December 2015
Poster Hall (Moscone South)
Dax Christian Soule1, Nick Bader2, Cayelan Carey3, Devin Castendyk4, Randy Fuller5, Catherine Gibson6, Rebekka Gougis-Darner7, Jennifer Klug8, Thomas Meixner9, Lucas E Nave10, Catherine O'Reilly11, David Richardson12 and Janet Stomberg7, (1)University of Washington, Seattle, WA, United States, (2)Whitman College, Geology, Walla Walla, WA, United States, (3)Virginia Polytechnic Institute and State University, Blacksburg, VA, United States, (4)University of Colorado at Boulder, Institute for Arctic and Alpine Research, Boulder, CO, United States, (5)Colgate University, Hamilton, NY, United States, (6)Skidmore College, Saratoga Springs, NY, United States, (7)Illinois State University, Biology, Normal, IL, United States, (8)Fairfield University, Fairfield, CT, United States, (9)University of Arizona, Tucson, AZ, United States, (10)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (11)Illinois State University, Department of Geography-Geology, Normal, IL, United States, (12)SUNY College at New Paltz, New Paltz, NY, United States
Abstract:
High-frequency sensor-based datasets are driving a paradigm shift in the study of environmental processes. The online availability of high-frequency data creates an opportunity to engage undergraduate students in primary research by using large, long-term, and sensor-based, datasets for science courses. Project EDDIE (Environmental Data-Driven Inquiry & Exploration) is developing flexible classroom activity modules designed to (1) improve quantitative and reasoning skills; (2) develop the ability to engage in scientific discourse and argument; and (3) increase students’ engagement in science. A team of interdisciplinary faculty from private and public research universities and undergraduate institutions have developed these modules to meet a series of pedagogical goals that include (1) developing skills required to manipulate large datasets at different scales to conduct inquiry-based investigations; (2) developing students’ reasoning about statistical variation; and (3) fostering accurate student conceptions about the nature of environmental science. The modules cover a wide range of topics, including lake physics and metabolism, stream discharge, water quality, soil respiration, seismology, and climate change. Assessment data from questionnaire and recordings collected during the 2014-2015 academic year show that our modules are effective at making students more comfortable analyzing data. Continued development is focused on improving student learning outcomes with statistical concepts like variation, randomness and sampling, and fostering scientific discourse during module engagement. In the coming year, increased sample size will expand our assessment opportunities to comparison groups in upper division courses and allow for evaluation of module-specific conceptual knowledge learned. This project is funded by an NSF TUES grant (NSF DEB 1245707).
