Integration of Computer-Assisted Methods and Human Interactions to Understand Lesson Plan Quality and Teaching to Advance Middle-Grade Mathematics Instruction

教材的策划和选择是数学教学中最复杂、最重要的组成部分之一。随着在线教学材料的不断增加，学校和教育工作者除了依赖教科书外，还依赖它们。虽然免费开放教育资源有其优势，但确保其质量和对教师和学生的效用仍然是最重要的问题。本研究的一个重要贡献是确定了使用尖端机器学习技术、有效数学教育知识和人类反馈的集成来衡量大量课程计划质量的方法。该项目的后期阶段包括采访教师，了解他们的教案实践，测量教案质量，分析学生的作业，为中学数学教案质量提供多个视角。使用机器学习来衡量课程计划的质量对数学教育领域具有变革性的潜力。该项目将确定使用自然语言处理和人类编码的混合方法来分析大量数学课程计划的方法。该项目以初中数学课程为重点，包括教师和学生的数据以及课程计划本身的文件。这项研究有三个研究目的。首先，通过组织由主要研究人员和熟练教师组成的专家计划，制定一个共享的概念框架，并规定中学数学高质量教案的维度。其次，通过应用最先进的计算机辅助方法（例如，机器学习）和人工编码来分析在知识共享许可下获得的大量数字课程计划，开发和验证捕获关键维度的措施。第三，对教师如何关注、解释和选择信息来创建自己的教案，以及教案质量与学生完成数学作业的关系进行探索性顺序混合方法研究。采用混合方法检验教案质量是推进数学教与学研究的一项重要创新。本项目由美国国家科学基金会EDU核心研究（ECR）项目资助。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM参与，以及STEM劳动力发展。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。

Planning and selecting instructional materials is one of the most complex and important components of mathematics teaching. As online instructional materials continue to proliferate, schools and educators are relying on them in addition to textbooks. While there are advantages to open educational resources that are freely available, questions about ensuring their quality and utility to teachers and students remain paramount. An important contribution of this study is identifying ways to measure the quality of large quantities of lesson plans using an integration of cutting-edge machine learning techniques, knowledge of effective mathematics education, and human feedback. Later phases of the project include interviewing teachers about their planning practices, measuring lesson plan quality, and analyzing students' work to provide multiple perspectives on mathematics lesson plan quality in the middle grades. The use of machine learning to measure lesson plan quality holds transformative potential for the field of mathematics education. The project will identify ways to analyze large numbers of mathematics lesson plans using a mixed methods approach of natural language processing and human coding. The project focuses on middle grades mathematics lesson and includes data from teachers and students as well as the lesson plan documents themselves. The study has three research aims. First, to develop a shared conceptual framework and specify dimensions of quality lesson plans for middle-grades mathematics by organizing an expert plan of leading researchers and skilled teachers. Second, to develop and validate measures to capture the key dimensions by applying state-of-the-art computer-assisted approaches (e.g., machine learning) and human coding to analyzing a large volume of digital lesson plans obtained under Creative Commons Licenses. Third, to conduct an exploratory sequential mixed-methods study of how teachers attend to, interpret, and select information to create their own lesson plans, and how lesson plan quality is related to students' completed mathematical work. The mixed methods approach to examining lesson plan quality is an important innovation for advancing research about mathematics teaching and learning. This project is supported by NSF's EDU Core Research (ECR) Program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development.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.