The creation of high-quality curricular materials requires knowledge of curriculum design and a considerable time commitment. Instructors often have limited time to dedicate to the creation of curricular materials. Additionally, the knowledge and skills needed to develop high-quality materials are often not taught to instructors. Furthermore, similar learning material is often prepared by multiple instructors working at separate institutions, leading to unnecessary duplication of effort and inefficiency that can impact quality. To address these problems, we established the HydroLearn platform and associated professional learning experiences for hydrology and water resources instructors. HydroLearn is an online platform for developing and sharing high-quality curricular materials, or learning modules, focused on hydrology and water resources. The HydroLearn team has worked with three cohorts of instructors from around the world who were dedicated to creating high-quality curricular materials to support both their students and the broader community. In order to overcome some of the aforementioned barriers, we tested and revised several different models of professional learning with these cohorts. These models ranged from (a) instructors working individually with periodic guidance from the HydroLearn team, to (b) small groups of instructors collaborating on topics of shared interests guided through an intensive HydroLearn training workshop. We found the following factors to contribute to the success of instructors in creating modules: (1) instructor pairs co-creating modules enhanced the usability and transferability of modules between universities and courses, (2) dedicating an intensive block of time (∼63 h over 9 days) to both learning about and implementing curriculum design principles, (3) implementing structures for continuous feedback throughout that time, (4) designing modules for use in one’s own course, and (5) instituting a peer-review process to refine modules. A comprehensive set of learning modules were produced covering a wide range of topics that target undergraduate and early graduate students, such as: floodplain analysis, hydrologic droughts, remote sensing applications in hydrology, urbanization and stormwater runoff, evapotranspiration, snow and climate, groundwater flow, saltwater intrusion in coastal regions, and stream solute tracers. We share specifics regarding how we structured the professional learning models, as well as lessons learned and challenges faced.
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Scalable Workflow-Driven Hydrologic Analysis in HydroFrame
The HydroFrame project is a community platform designed to facilitate integrated hydrologic modeling across the US. As a part of HydroFrame, we seek to design innovative workflow solutions that create pathways to enable hydrologic analysis for three target user groups: the modeler, the analyzer, and the domain science educator. We present the initial progress on the HydroFrame community platform using an automated Kepler workflow. This workflow performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We demonstrate how different modules of the workflow can be reused and repurposed for the three target user groups. The Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions, and hardware system configuration. In addition, we aim to optimize the utilization of large-scale computational resources to adjust to the needs of all three user groups. Towards this goal, we present a design that leverages provenance data and machine learning techniques to predict performance and forecast failures using an automatic performance collection component of the pipeline.
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- Award ID(s):
- 1835855
- NSF-PAR ID:
- 10200442
- Editor(s):
- Krzhizhanovskaya, Valeria V.; Závodszky, Gábor; Lees, Michael H.; Dongarra, Jack J.; Sloot, Peter M.; Brissos, Sérgio; Teixeira, João
- Date Published:
- Journal Name:
- Computational Science ICCS 2020 (Springer International Publishing)
- Volume:
- 2020
- Page Range / eLocation ID:
- 276-289
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-50371-0_20
