Designing Chatbot-Based Learning Experiences to Enhance Preservice Elementary Teachers’ Responsiveness to Student Mathematical Thinking

本项目旨在通过提高职前小学教师在数学方面的反应性教学技能，从而改善学生的STEM学习，从而为国家利益服务。具体来说，肯尼索州立大学和密苏里-哥伦比亚大学的主要研究人员将合作，通过使用基于人工智能（AI）的聊天机器人，在他们的职前教师中开发和培养响应性的数学教学技能。在学生的早期阶段奠定坚实的数学基础对于STEM劳动力的发展至关重要。响应式教学提高了学生的数学推理能力和积极的数学态度。然而，许多职前教师在与真正的学生一起工作之前，实践响应式教学的机会有限。在这个项目中，研究人员将探索聊天机器人如何作为一个创新的学习平台，为实践和完善响应式教学技能提供广泛的机会，比如注意、引导和探索学生的思维。该项目的成果将扩大数学知识基础，扩大教师备课的最佳做法。使用基于设计的研究原则，该项目的目标是在教学分数的背景下开发，制定，评估和完善三个模块，分数是早期数学学习的关键概念。在每个模块中，用户将：1)呈现教学实践的表现，2)参与实践的分解，3)参与与聊天机器人的真实互动，聊天机器人既是共享特定数学策略的虚拟学生，也是支持互动的导师老师，4)反思他们的互动。该项目的活动范围包括：1)开发包括基于人工智能的聊天机器人在内的教学模块；2)传播这些模块以产生更广泛的影响。该过程需要通过三个迭代开发、制定、评估和细化模块。将收集各种数据来改进聊天机器人并评估其功效。这将产生可用于其他数学主题和STEM领域的教师教育的设计原则。NSF IUSE: EDU项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过其参与学生学习轨道，该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。

This project aims to serve the national interest by improving preservice elementary teachers’ responsive teaching skills in mathematics, leading to improved STEM learning of their students. Specifically, principal investigators at Kennesaw State University and the University of Missouri-Columbia, will collaborate to develop and nurture responsive teaching skills in mathematics among their preservice teachers by using Artificial Intelligence (AI)-based chatbots. Laying a solid mathematical foundation in students’ early years is essential to the development of a STEM workforce. Responsive teaching promotes students’ mathematical reasoning abilities and positive attitudes toward mathematics. However, many preservice teachers have limited opportunities to practice responsive teaching prior to working with real students. In this project, researchers will explore how chatbots can serve as an innovative learning platform to provide extensive opportunities to practice and refine responsive teaching skills such as noticing, eliciting, and exploring student thinking. The outcomes of the project will advance the knowledge base and expand best practices for teacher preparation in mathematics.Using principles of design-based research, the project’s goals are to develop, enact, evaluate, and refine three modules in the context of teaching fractions, a critical concept in early mathematics learning. In each module, users will: 1) be presented with a representation of teaching practice, 2) engage in a decomposition of practice, 3) participate in authentic interaction with a chatbot that serves as both a virtual student sharing a particular mathematical strategy and a mentor teacher supporting the interaction, and 4) reflect on their interactions. The scope of the project's activities includes: 1) developing the instructional modules including AI-based chatbots and 2) disseminating the modules for broader impact. The process entails developing, enacting, evaluating, and refining the modules through three iterations. Various data will be collected to improve the chatbots and evaluate their efficacy. This will generate design principles that can be used in teacher education across other mathematical topics and STEM fields. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the 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.