Data from: Building communities of teaching practice and data-driven open education resources with NEON faculty mentoring networks
Naithani, Kusum; Jones, Megan; Grayson, Kristine (2022), Data from: Building communities of teaching practice and data-driven open education resources with NEON faculty mentoring networks, Dryad, Dataset, https://doi.org/10.5061/dryad.f1vhhmh03
With the growing availability and accessibility of big data in ecology, we face an urgent need to train the next generation of scientists in data science practices and tools. One of the biggest barriers for implementing a data-driven curriculum in undergraduate classrooms is the lack of training and support for educators to develop their own skills and time to incorporate these principles into existing courses or develop new ones. Alongside the research goals of the National Ecological Observatory Network (NEON), providing education and training are key components for building a community of scientists and users equipped to utilize large-scale ecological and environmental data. To address this need, the NEON Data Education Fellows program formed as a collaborative Faculty Mentoring Network (FMN) between scientists from NEON and university faculty interested in using NEON data and resources in their ecology classrooms. Like other FMNs, this group has two main goals: 1) to provide tools, resources, and support for faculty interested in developing data-driven curriculum, and (2) to make teaching materials that have been implemented and tested in the classroom available as open educational resources for other educators. We hosted this program using an open education and collaboration platform from the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project. Here, we share lessons learned from facilitating five FMN cohorts and emphasize the successes, pitfalls, and opportunities for developing open education resources through community-driven collaborations.
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National Science Foundation, Award: 2026815
National Science Foundation, Award: 1702701
National Science Foundation, Award: 1730122
National Science Foundation, Award: 1446258
National Science Foundation, Award: 1446269
National Science Foundation, Award: 1446284