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Better understanding of hydrologic process through data-driven learning facilitated by collaborative open web-based platforms
  • +2
  • Belize Lane,
  • Irene Garousi-Nejad,
  • Melissa Gallagher,
  • Dave Tarboton,
  • Emad Habib
Belize Lane
Utah State University

Corresponding Author:belize.lane@usu.edu

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Irene Garousi-Nejad
Utah State University
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Melissa Gallagher
University of Houston System
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Dave Tarboton
Utah State University
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Emad Habib
University of Louisiana at Lafayette
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The era of "big data'' promises to provide new hydrologic insights, and open web-based platforms are being developed and adopted by the hydrologic science community to harness these datasets and data services. This shift accompanies advances in hydrology education and the growth of web-based hydrology learning modules, but their capacity to utilize emerging open platforms and data services to enhance student learning through data-driven activities remains largely untapped. Given that generic equations may not easily translate into local or regional solutions, teaching students to explore how well models or equations work in particular settings or to answer specific problems using real data is essential. This paper introduces an open web-based learning module developed to advance data-driven hydrologic process learning, targeting upper level undergraduate and early graduate students in hydrology and engineering. The module was developed and deployed on the HydroLearn open educational platform, which provides a formal pedagogical structure for developing effective problem-based learning activities. We found that data-driven learning activities utilizing collaborative open web platforms like HydroShare and CUAHSI JupyterHub computational notebooks allowed students to access and work with datasets for systems of personal interest and promoted critical evaluation of results and assumptions. Initial student feedback was generally positive, but also highlights challenges including trouble-shooting and future-proofing difficulties and some resistance to open-source software and programming. Opportunities to further enhance hydrology learning include better articulating the myriad benefits of open web platforms upfront, incorporating additional user-support tools, and focusing methods and questions on implementing and adapting notebooks to explore fundamental processes rather than tools and syntax. The profound shift in the field of hydrology toward big data, open data services and reproducible research practices requires hydrology instructors to rethink traditional content delivery and focus instruction on harnessing these datasets and practices in the preparation of future hydrologists and engineers.
18 Mar 2021Submitted to Hydrological Processes
22 Mar 2021Submission Checks Completed
22 Mar 2021Assigned to Editor
23 Mar 2021Reviewer(s) Assigned
05 May 2021Review(s) Completed, Editorial Evaluation Pending
18 May 2021Editorial Decision: Revise Minor
07 Jun 20211st Revision Received
18 Jun 2021Submission Checks Completed
18 Jun 2021Assigned to Editor
18 Jun 2021Reviewer(s) Assigned
18 Jun 2021Review(s) Completed, Editorial Evaluation Pending
18 Jun 2021Editorial Decision: Accept