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  1. null (Ed.)
    Engineering graduates need a deep understanding of key concepts in addition to technical skills to be successful in the workforce. However, traditional methods of instruction (e.g., lecture) do not foster deep conceptual understanding and make it challenging for students to learn the technical skills, (e.g., professional modeling software), that they need to know. This study builds on prior work to assess engineering students’ conceptual and procedural knowledge. The results provide an insight into how the use of authentic online learning modules influence engineering students’ conceptual knowledge and procedural skills. We designed online active learning modules to support and deepen undergraduate students’ understanding of key concepts in hydrology and water resources engineering (e.g., watershed delineation, rainfall-runoff processes, design storms), as well as their technical skills (e.g., obtaining and interpreting relevant information for a watershed, proficiency using HEC-HMS and HEC-RAS modeling tools). These modules integrated instructional content, real data, and modeling resources to support students’ solving of complex, authentic problems. The purpose of our study was to examine changes in students’ self-reported understanding of concepts and skills after completing these modules. The participants in this study were 32 undergraduate students at a southern U.S. university in a civil engineering senior design course who were assigned four of these active learning modules over the course of one semester to be completed outside of class time. Participants completed the Student Assessment of Learning Gains (SALG) survey immediately before starting the first module (time 1) and after completing the last module (time 2). The SALG is a modifiable survey meant to be specific to the learning tasks that are the focus of instruction. We created versions of the SALG for each module, which asked students to self-report their understanding of concepts and ability to implement skills that are the focus of each module. We calculated learning gains by examining differences in students’ self-reported understanding of concepts and skills from time 1 to time 2. Responses were analyzed using eight paired samples t-tests (two for each module used, concepts and skills). The analyses suggested that students reported gains in both conceptual knowledge and procedural skills. The data also indicated that the students’ self-reported gain in skills was greater than their gain in concepts. This study provides support for enhancing student learning in undergraduate hydrology and water resources engineering courses by connecting conceptual knowledge and procedural skills to complex, real-world problems. 
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  2. null (Ed.)
  3. 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.

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