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

教授学生处理大量数据所需的数学和统计技能是当代STEM教育的一个挑战。该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数据。四所院校（伊利诺伊州立大学、卡尔顿学院、亚利桑那大学和纽约市立大学皇后学院）的教师将合作开发这些模块，使学生能够使用真实的数据提问和回答问题。通过作为一个社区共同努力，使用这些模块的教师可以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。模块将用于教授STEM专业和非专业学生，以帮助建立一个知情的公民，可以理解和做出基于科学的决策。该项目旨在开发一种新的方式来建立一个教师社区，以及开发材料，可以在广泛的机构中加强本科阶段的数据教学。 该社区将开发课程和辅助材料，以加强地球科学相关领域本科生的定量和统计技能教学。该项目还将促进教师参与材料和专业发展活动，旨在促进教学方向，有利于以大型数据集为基础的探究式教学法。将教师的教学取向从直接教学转变为基于探究的教学，可以提高学生与量化素养相关的学习成果。该项目将研究将教学量化技能与现实世界的数据和基于社区的方法联系起来，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策略; 3）新的适应和成功课程的例子，实施和材料排序;（4）专业发展和项目宣传活动，促进这些新方法的广泛采用。该项目旨在提供证据，说明如何发展和吸引广泛的用户群体，以及教师的教学取向如何从直接转向基于探究的。将收集和分析各种指标（例如参与者人口统计数据;网站浏览量;社交网络分析）和调查数据。教员的反馈意见将为改进单元材料和调查教学法培训提供信息。共同开发和测试的模块将公开提供。该项目还旨在培养一批受过定量教学技能和工具培训的教师骨干，以及对教材的合作开发如何可能导致教学方向变化的深入理解。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.