An open web‐based module developed to advance data‐driven hydrologic process learning. Issue 7 (6th July 2021)

• Record Type: Journal Article
• Authors: Lane, Belize; Garousi‐Nejad, Irene; Gallagher, Melissa A.; Tarboton, David G.; Habib, Emad
• Is Part Of: Hydrological processes. Volume 35:Issue 7(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-06
• Subjects: CUAHSI -- data‐driven learning -- education -- HydroLearn -- HydroShare -- JupyterHub -- physical hydrology -- web‐based; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.14273
• Languages: English
• ISSNs: 0885-6087

Abstract

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. Abstract : We developed a web‐based learning module on physical hydrology to enhance student understanding of hydrologic processes through authentic, data‐driven learning activities. The module combines HydroLearn's formal pedagogical structure with the use of collaborative open web‐based data services like CUAHSI HydroShare and JupyterHub to access data and computational resources. The flexible data and programming language integration capacity of these platforms facilitates student‐centred, data‐driven hydrologic process learning and provides experience in good data management and reproducibility practices.