Collaborative Research: Environmental Data-Driven Inquiry and Exploration (EDDIE): Using Large Datasets to Build Quantitative Reasoning

在当代STEM教育中，教授学生处理大量数据所需的数学和统计技能是一项挑战。这个项目旨在通过开发课堂模块来应对这一挑战，使本科生能够探索来自生物学、地质学和环境科学的真实世界数据。四个机构(伊利诺伊州立大学、卡尔顿学院、亚利桑那大学和纽约大学皇后学院)的教职员工将合作开发这些模块，这将使学生能够使用真实数据来提问和回答问题。通过作为一个社区进行合作，使用这些模块的教师可能会找到改进定量推理技能教学的方法，并有可能改变数据科学的教学方式。模块将被用来教授STEM专业和非专业的学生，以帮助建立能够理解和做出科学决策的知情公民。该项目寻求开发一种新的方式来建立教师社区，以及开发可以在广泛的机构中加强本科生水平的数据教学和学习的材料。该社区将开发课程和辅助材料，以加强对地球科学相关领域的本科生的量化和统计技能的教学。该项目还将促进教师参与材料和专业发展活动，旨在促进教学导向，支持基于探究的教学，并使用大量数据集。将教师的教学取向从直接教学转向探究式教学，可能会改善学生与数量素养相关的学习结果。该项目将调查将教学量化技能与真实世界数据和基于社区的方法联系起来，其中包括1)通过专题研讨会收集和记录社区需求；2)开发和评估新的教学资源和教学战略；3)新的适应和成功的课程、实施和材料排序的范例；以及4)促进广泛采用这些新方法的专业发展和项目宣传活动。该项目寻求产生证据，说明如何发展和吸引广泛的用户社区，以及教师的教学取向如何从直接转向以探究为基础。将收集和分析各种指标(例如参与者人口统计数据；网站页面浏览量；社交网络分析)和调查数据。教师的反馈将为模块材料的改进和探究式教学法的培训提供信息。共同开发和测试的模块将公开提供。该项目还旨在培养一支在传授量化技能方面接受过技能和工具培训的教职员工队伍，以及对合作开发教材如何导致教学方向变化的高级理解。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和更广泛的影响审查标准进行评估，被认为值得支持。

Teaching students the mathematical and statistical skills needed to work with large amounts of data is a challenge in contemporary STEM education. This project aims to address that challenge by developing classroom modules that enable undergraduate students to explore real-world data from biology, geology, and environmental science. Faculty at four institutions (Illinois State University, Carleton College, the University of Arizona, and CUNY-Queens College) will collaborate to develop these modules, which will enable students to use real data to ask and answer questions. By working together as a community, instructors who use these modules may identify ways to improve teaching of quantitative reasoning skills, with the potential to transform how data science is taught. Modules will be used to teach both STEM majors and non-majors, to help build an informed citizenry that can understand and make science-based decisions.This project seeks to develop a novel way to build a community of instructors, as well as develop materials that may enhance the teaching and learning of data at the undergraduate-level at a broad range of institutions. This community will develop curricular and supporting materials to enhance teaching of quantitative and statistical skills to undergraduate students in earth science-related fields. The project will also promote faculty engagement with materials and professional development activities designed to foster pedagogical orientation favoring inquiry-based pedagogy with large datasets. Shifting pedagogical orientation of instructors from direct instruction to inquiry-based instruction may improve student learning outcomes related to quantitative literacy. The project will investigate connecting teaching quantitative skills with real-world data and a community-based approach that includes 1) collecting and documenting community needs through topical workshops; 2) the development and evaluation of new teaching resources and instructional strategies; 3) new adaptations and examples of successful curricula, implementation, and material sequencing; and 4) professional development and project propagation activities promoting widespread adoption of these new approaches. The project seeks to generate evidence about how to develop and engage a broad community of users, as well as how an instructor's pedagogical orientation shifts from direct to inquiry-based. A variety of metrics (e.g. participant demographics; website pageviews; social network analytics) and survey data will be gathered and analyzed. Feedback from the instructors will inform improvements of the module materials and the training in inquiry-based pedagogy. The co-developed and tested modules will be publicly available. The project also aims to produce a cadre of faculty members who are trained in skills and tools for teaching quantitative skills, as well as an advanced understanding of how the collaborative development of teaching materials might lead to a change in pedagogical orientation.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.