Collaborative Research: Developing and Exploring Mathematical Modeling Curricula for Preservice K-8 Teachers

在美国国家科学基金“改善本科STEM教育计划：教育与人力资源”（IUSE: EHR）的支持下，本探索与设计项目旨在通过开发、实施和测试将教育研究应用于学科教学的创造性方法，为国家利益服务。该项目将开发课程，将STEM数学建模知识的最新进展引入本科小学教师教育。数学建模是利用数学来理解现实世界的真实问题的过程，它为所有学生提供了强大的学习机会。尽管建模越来越重要，但很少有在职前的K-8教师知道什么是建模，或者有过第一手的经验。对他们来说，在将建模融入教学之前，以学习者的身份体验建模是很重要的。本项目旨在通过设计、制定和完善职前教师数学建模模块来解决这一需求。该项目将使用设计理论方法来考察建模教学的多个方面，而不仅仅是内容知识。这个合作项目将涉及蒙大拿州立大学和马凯特大学，以及两所大学的各种本科课程。该项目将从各种来源收集数据，包括课堂视频数据、职前教师构建的模型和期刊。项目团队将通过决策建模过程使用这些数据来记录职前教师如何通过建模周期，注意具体的选择、行动和倾向。然后，这些信息将用于改进开发的教学模块，并为这些模块提供书面和视频注释。基于这一分析，该项目旨在解决：1)教师教育者如何支持职前教师对数学建模属性的理解；2)职前教师对建模的理解是如何跨模块发展的；3)支持职前教师从事数学建模及其相关实践动机的因素。该项目开发的模块和相应的视频和注释将可在线访问，并将通过网络研讨会和会议等渠道与数学教师教育者共享。通过这些模块的传播，数学教师教育者将有可见的资源来帮助将数学建模整合到一系列现有的课程中，包括本科数学建模课程、职前教师方法课程和本科教师数学课程。更广泛地说，这个项目有可能通过改善本科生的准备来造福社会，他们将成为STEM领域的多学科教师。NSF IUSE: EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该项目属于IUSE: EHR参与学生学习轨道，通过该轨道，资助计划支持有前途的实践和工具的创建、探索和实施。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the NSF Improving Undergraduate STEM Education Program: Education and Human Resources (IUSE: EHR), this Exploration & Design project aims to serve the national interest by developing, implementing, and testing creative approaches for adoption of education research into disciplinary teaching. The project will develop curricula that bring recent advances in STEM knowledge of mathematical modeling into undergraduate elementary teacher education. Mathematical modeling is the process of using mathematics to make sense of real-world, authentic problems and it provides powerful learning opportunities for all students. Although modeling is of growing importance, few preservice K-8 teachers have knowledge of what modeling is or have experienced it firsthand. It is important for them to experience modeling as learners before they can integrate modeling into teaching. This project aims to address this need through the design, enactment, and refinement of mathematical modeling modules for preservice teachers. The project will use design-theory methodology to examine multiple facets of the teaching and learning of modeling beyond content knowledge. This collaborative project will involve Montana State University and Marquette University, and various undergraduate courses at each institution. The project will gather data from various sources, including classroom video data, and preservice teachers' constructed models and journals. The project team will use this data to document, through a Decision Modeling Process, how preservice teachers move through the modeling cycle, noting specific choices, actions, and dispositions. This information will then be used to refine the developed instructional modules and provide written and video annotations for these modules. Based on this analysis, the project seeks to address: 1) how teacher educators can support preservice teachers' understanding of the attributes of mathematical modeling; 2) how preservice teachers' understanding of modeling develops across modules; and 3) factors that support preservice teachers' motivation to engage in mathematical modeling and its related practices. The modules and corresponding videos and annotations developed by the project will be accessible online and will be shared with mathematics teacher educators through outlets such as webinars and conferences. Through dissemination of the modules, mathematics teacher educators will have visible resources to aid in integrating mathematical modeling into a range of existing courses, including undergraduate mathematical modeling courses, preservice teacher methods courses, and undergraduate mathematics courses for teachers. More broadly, this project has potential to benefit society by improving the preparation of undergraduate students, who will become multi-disciplinary teachers in a STEM field. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the IUSE: EHR Engaged Student Learning track, through which the funding program supports the creation, exploration, and implementation of promising practices and tools.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.