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

在NSF改善本科STEM教育计划：教育与人力资源(IUSE：EHR)的支持下，这个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新方法来服务于国家利益。该项目将开发将STEM数学建模知识的最新进展带入本科小学教师教育的课程。数学建模是使用数学来理解真实世界中真实的问题的过程，它为所有学生提供了强大的学习机会。尽管建模的重要性与日俱增，但很少有K-8职前教师了解什么是建模或亲身经历过。对他们来说，在将建模融入教学之前，作为学习者体验建模是很重要的。本项目旨在通过为职前教师设计、实施和改进数学建模模块来满足这一需求。该项目将使用设计理论方法论来检查超越内容知识的建模教学的多个方面。这个合作项目将涉及蒙大拿州立大学和马奎特大学，以及每个机构的各种本科课程。该项目将从各种来源收集数据，包括课堂视频数据，以及教师构建的模型和日记。项目团队将使用这些数据通过决策建模过程记录职前教师如何在建模周期中移动，并记录具体的选择、行动和处置。这些信息将被用来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，本项目试图解决：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)职前教师对数学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的动机的因素。该项目开发的单元以及相应的视频和注释将可在线获取，并将通过网络研讨会和会议等渠道与数学教师教育工作者分享。通过传播这些单元，数学教师教育工作者将拥有可见的资源，以帮助将数学建模融入一系列现有课程，包括本科数学建模课程、职前教师方法课程和教师本科数学课程。更广泛地说，这个项目有可能通过改善本科生的准备工作而造福社会，这些本科生将成为STEM领域的多学科教师。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。这个项目是在IUSE：EHR参与式学生学习轨道上进行的，资助计划通过该轨道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。

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.