Document Type
Article
Author ORCID Identifier
Belize A. Lane https://orcid.org/0000-0003-2331-7038
Irene Garousi-Nejad https://orcid.org/0000-0003-2929-3946
David G. Tarboton https://orcid.org/0000-0002-1998-3479
Journal/Book Title/Conference
Hydrological Processes
Volume
35
Issue
7
Publisher
John Wiley & Sons Ltd.
Publication Date
6-22-2021
Award Number
NSF, Division of Undergraduate Education (DUE) 1725989
Funder
NSF, Division of Undergraduate Education (DUE)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Abstract
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 article introduces an open web-based 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 CUAHSI HydroShare and JupyterHub to store and run 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 highlighted challenges including trouble-shooting and future-proofing difficulties and some resistance to programming and new software. Opportunities to further enhance hydrology learning include better articulating the benefits of coding and 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.
Recommended Citation
Lane, B., Garousi-Nejad, I., Gallagher, M. A., Tarboton, D. G., & Habib, E. (2021). An open web-based module developed to advance data-driven hydrologic process learning. Hydrological Processes, 35( 7), e14273. https://doi.org/10.1002/hyp.14273
