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RCN-UBE: Biological and Environmental Data Education Network: Preparing Instructors to Integrate Data Science into Undergraduate Biology and Environmental Science Curricula

RCN-UBE: Biological and Environmental Data Education Network: Preparing Instructors to Integrate Data Science into Undergraduate Biology and Environmental Science Curricula
RCN-UBE:生物和环境数据教育网络:培养教师将数据科学融入本科生物学和环境科学课程
批准号:
2120609
负责人:
Matthew Aiello-Lammens
金额:
$49.94万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-10-01 至 2026-09-30

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This project aims to serve the national interest by building and supporting an engaged community of college instructors trained in integrating data science skills across introductory biology and environmental science curricula. Students need foundational and repeated exposure to data science skills that are necessary for success in Science, Technology, Engineering, and Math fields and beyond. However, there are barriers to incorporating data science skills into established biology and environmental science curricula, including a knowledge gap between the skills students need and the training faculty have, curricula that are already perceived as overcrowded, confusion over what the key skills are, and a lack of confidence in teaching these skills. The BEDE (Biological and Environmental Data Education) Network will address the unmet need for data science skills and overcome barriers through professional development opportunities for biology and environmental science instructors. The BEDE Network will develop and deliver data science skills- and pedagogy-focused workshops to instructors, develop and disseminate guides for integrating these skills into biology and environmental science courses to instructors and departments, and will foster a collaborative community of educators via a combination of virtual and in-person meetings and asynchronous online interaction and resource sharing. These activities align with the NSF IUSE intention to enhance and improve undergraduate STEM education in the US to prepare students for a competitive workforce that increasingly requires data science skills.The BEDE Network will conduct activities in four main areas: (1) convene annual network meetings, (2) develop and deliver “train the teacher” and “train the trainer” workshops, (3) design and publish a curriculum guide, and (4) establish an active and diverse community of practicing educators that are incorporating key data science skills into their undergraduate biology and/or environmental science courses. Six main data science areas will be covered in the activities (data management, data analysis, data visualization, programming, modeling, and reproducibility), with ethical frameworks applied to each to anticipate and address potential challenges that may arise during classroom activities. Each activity area will include regular assessments conducted both internally and externally including annual evaluations, pre/post surveys, and reflections. The BEDE Network will be an inclusive and diverse community drawing educators from multiple institution types, career paths, and career stages, and will be designed to last beyond the 5-year term of the RCN-UBE grant. By specifically focusing on undergraduate instructors, the BEDE Network will be able to train educators who in turn are positioned to benefit a broad population of students including STEM majors and non-majors, and from groups that are currently underrepresented in the sciences. This more inclusive group of students will learn and use data science skills in their courses that will prepare them for success in the “Big Data” world we are living in, no matter what their chosen career will be. This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for Education and Human Resources, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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