Collaborative Research: Advancing Bayesian Thinking in STEM

该项目旨在通过注重增加使用贝叶斯方法来改进统计教学，从而服务于国家利益。处理不确定性的复杂性是科学过程的重要组成部分。像科学家一样，STEM的学生需要在他们的科学实践中从数据中得出严格的结论。这个项目的前提是，在STEM课程中更广泛地纳入贝叶斯方法可以帮助学生理解科学的不确定性。为了支持这些方法的更广泛使用，该项目计划建立一个STEM教育工作者社区，他们可以通过引入新的贝叶斯方法教学材料来改变他们的课程。该项目团队打算为来自其他机构的STEM讲师开发和提供专业发展计划，重点是贝叶斯方法的使用和教学。此外，在编写教学材料方面，项目团队将指导教师团队。该项目将通过社交媒体、期刊出版物和会议演示向科学教育界传播教学材料和项目成果。这个项目的目标是通过针对不同STEM领域的跨学科课程教师能力建设计划，使贝叶斯方法在本科水平尽可能容易获得。通过招募多样化的STEM教师队伍，该项目将：1)增加了解贝叶斯方法的本科生人数；2)通过培训和社区建设，提高STEM教师使用贝叶斯方法的能力；3)开发和丰富教学材料，展示贝叶斯方法在STEM领域的使用。为了实现这些目标，加州大学欧文分校、瓦萨学院和杜克大学这三所合作机构将提供为期一周的教师夏季培训新兵训练营。到新兵训练营结束时，预计讲师学员将熟悉使用贝叶斯方法回答科学问题，使用适当的软件教授贝叶斯方法，并设计支持学习贝叶斯方法的课堂活动和评估。从新兵训练营挑选出来的教员将由项目组指导编写贝叶斯教学材料，特别是使用他们所在领域的科学数据。利用调查和学习评估，该项目将评估夏季新兵训练营的效果。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与式学生学习路径，该计划支持创建、探索和实施有前途的实践和工具。该项目也得到了NSF IUSE：HSI计划的支持，该计划的目标是提高STEM本科教育的质量，并增加STEM专科或学士学位学生的招生、留住和毕业率。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to serve the national interest by improving statistics instruction through a focus on increasing access to Bayesian methods. Dealing with the complexity of uncertainty is an important part of the scientific process. Like scientists, STEM students need to derive rigorous conclusions from data in their science practice. This project is based upon the premise that wider inclusion of Bayesian methods in STEM curricula can help students understand scientific uncertainty. To support the wider use of these methods, the project plans to build a community of STEM educators who can transform their courses by introducing new instructional materials for Bayesian methods. The project team intends to develop and offer a professional development program for STEM instructors from other institutions that focuses on the use and teaching of Bayesian methods. In addition, teams of instructors will be mentored by the project team in the development of instructional materials. The project will disseminate the instructional materials and project results to the science education community through social media, journal publications, and conference presentations. This goal of this project is to make Bayesian methods as accessible as possible at the undergraduate level through a cross-disciplinary curricular instructor capacity-building program for different STEM fields. Through recruitment of a diverse body of STEM instructors, the project will: 1) Increase the number of undergraduate students who understand Bayesian methods; 2) Enhance the capacity of STEM instructors in Bayesian methods through training and community building; 3) Develop and enrich teaching and learning materials that showcase the use of Bayesian methods in STEM fields. To achieve these objectives, the three collaborating institutions, University of California Irvine, Vassar College, and Duke University, will offer a week-long instructor summer training boot camp. By the end of the boot camp, it is expected that instructor participants will be comfortable using Bayesian methods in answering scientific questions, using appropriate software for teaching Bayesian methods, and designing classroom activities and assessments that support the learning of Bayesian methods. Selected instructors from the boot camp will be mentored by the project team in the development of Bayesian teaching and learning materials, specifically using scientific data from their fields. Using surveys and learning assessments, the project will assess the effectiveness of the summer boot camp. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This project is also supported by the NSF IUSE:HSI program, which has the goals of enhancing the quality of undergraduate STEM education, and increasing the recruitment, retention, and graduation rates of students pursuing associate’s or baccalaureate degrees in STEM.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.