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: Pace University New York Campus
• 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
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120609&HistoricalAwards=false
• 关键词: RCN; UBE; Biological; Environmental; Data

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.

该项目旨在通过建立和支持一个参与的大学教师社区来服务于国家利益，该社区接受了将数据科学技能融入生物学和环境科学课程的培训。学生需要基础和反复接触数据科学技能，这是在科学，技术，工程和数学领域取得成功所必需的。然而，将数据科学技能纳入既定的生物学和环境科学课程存在障碍，包括学生所需技能与培训教师之间的知识差距，已经被认为过于拥挤的课程，对关键技能的困惑以及对教授这些技能缺乏信心。BEDE（生物和环境数据教育）网络将通过为生物和环境科学教师提供专业发展机会，解决对数据科学技能的未满足需求，并克服障碍。BEDE网络将为教师开发和提供以数据科学技能和教学为重点的研讨会，为教师和部门开发和传播将这些技能融入生物学和环境科学课程的指南，并将通过虚拟和面对面会议以及异步在线互动和资源共享相结合，促进教育工作者的协作社区。这些活动符合NSF IUSE的意图，即加强和改善美国的本科STEM教育，使学生为越来越需要数据科学技能的竞争力劳动力做好准备。BEDE网络将在四个主要领域开展活动：（1）召开年度网络会议，（2）制定和举办“培训教师”和“培训培训员”讲习班，（3）设计和出版课程指南，以及（4）建立一个活跃而多样化的实践教育者社区，将关键的数据科学技能融入他们的本科生物学和/或环境科学课程。活动将涵盖六个主要的数据科学领域（数据管理，数据分析，数据可视化，编程，建模和再现性），每个领域都应用了道德框架，以预测和解决课堂活动中可能出现的潜在挑战。每个活动领域都将包括内部和外部进行的定期评估，包括年度评价、前后调查和反思。BEDE网络将是一个包容性和多元化的社区，吸引来自多种机构类型，职业道路和职业阶段的教育工作者，并将设计为超过RCN-UBE赠款的5年期限。通过特别关注本科教师，BEDE网络将能够培训教育工作者，这些教育工作者反过来又能够使广大学生受益，包括STEM专业和非专业学生，以及目前在科学领域代表性不足的群体。这个更具包容性的学生群体将在他们的课程中学习和使用数据科学技能，这将为他们在我们生活的“大数据”世界中取得成功做好准备，无论他们选择的职业是什么。该项目由生物基础设施部生物科学理事会和本科教育部教育和人力资源理事会共同资助，作为应对本科生物学教育中的愿景和变革所带来的挑战的努力的一部分：行动呼吁（http：//visionandchange/finalreport/）该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，更广泛的影响审查标准。