Intelligent, Adaptive Program with Just-in-time Feedback for Preservice Teachers

该项目旨在通过一个带有个性化反馈的互动项目，改善职前教师的循证教学实践，从而服务于国家利益。教师对数学概念的理解，以及他们对学生如何学习的理解，以及他们的教学工具和实践技能，对于实施将引导学生掌握概念的教学实践是至关重要的。然而，职前教师可能会有不同程度的学习机会，这取决于他们所在的地点或他们注册的教师准备计划。该项目将通过一个方案来解决这一问题，该方案可供中小学职前教师随时随地使用，重点是比率和比例关系的教学，这是学习者长期面临的挑战。这项工作的基础是对一个被发现有效改善在职教师数学教学和学生数学学习的原型进行改编。培养一支具有强化的具体内容知识和教学技能的教师队伍，可以为学生创造学习环境，使他们掌握STEM概念。该项目希望增进对职前教师学习的理解，并就使用人工智能的互动程序如何提高职前教师的知识和实践提供新的知识。该项目的目标是通过开发一个使用自然语言处理提供即时、个性化反馈的程序来提高职前教师的数学内容知识(CK)、教学内容知识(PCK)和教学技能。教师的CK和PCK被认为是教师实施引导学生发展深层概念知识的教学实践的关键。虽然该方案的原型已被发现在改善在职教师的做法和学生的学习方面有效，但如何为职前教师设计类似的方案的问题尚未得到审查，因为他们的知识和经验与在职教师不同。该项目还将研究自然语言处理在多大程度上能够可靠地检测到职前教师的反应。该计划将根据数学方法课程的教师和职前教师的反馈，通过互动设计周期进行开发和完善。随后，将通过一项随机对照试验，对来自全国至少八所大学的200名职前教师进行抽样调查，以检验该项目在职前教师知识和教学实践方面的有效性。以下问题将被调查：智能的、交互式的、基于计算机的即时反馈程序在多大程度上提高了职前教师的CK和PCK？智能的、交互式的、基于计算机的、及时反馈的程序对职前教师的比例和比例关系教学有什么影响？一个专家咨询委员会将在项目的三个阶段提供独立的反馈。该程序的最终版本将在网上免费提供。NSF IUSE：EDU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。通过参与的学生学习路径，该计划支持有前景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。

This project aims to serve the national interest by improving preservice teachers’ evidence-based teaching practices through an interactive program with personalized feedback. Teachers’ conceptual understanding of mathematics as well as their understanding of how students learn, and their repertoire of instructional tools and practices, are essential for implementing teaching practices that will lead students to develop command of concepts. Yet preservice teachers may have varying degrees of access to learning opportunities, depending on their location or the teacher preparation program in which they enroll. This project will address this issue through a program that is accessible to elementary and middle grade preservice teachers anywhere and at any time focused on teaching of ratios and proportional relationships, an area of persistent challenge for learners. Adaptation of a protype that was found effective in improving in-service teachers’ mathematics teaching and students’ mathematics learning is a basis for this work. Preparation of a teaching workforce with strengthened content-specific knowledge and teaching skills can create learning environments for students to attain command of STEM concepts. This project hopes to advance understanding of preservice teachers’ learning and contribute new knowledge about how an interactive program that uses artificial intelligence can improve preservice teachers’ knowledge and practices.The goal of the project is to enhance preservice teachers’ mathematical content knowledge (CK), pedagogical content knowledge (PCK), and teaching skills through the development of a program that uses natural language processing to provide instant, personalized feedback. Teachers’ CK and PCK have been identified as essential for teachers to implement teaching practices that lead students in developing deep conceptual knowledge. Although a prototype of the program has been found effective in improving in-service teachers’ practices and their students’ learning, the issue of how a similar program might be designed for preservice teachers, who differ from in-service teachers in terms of their knowledge and experience, has not been examined. The extent to which natural language processing can reliably detect preservice teachers’ responses will also be researched in the project. The program will be developed and refined through an interactive design cycle, based on feedback from instructors of mathematics methods courses and from preservice teachers. The effectiveness of the program on preservice teachers’ knowledge and teaching practices will then be examined through a randomized controlled trial with a sample of 200 preservice teachers drawn from at least eight universities across the nation. The following questions will be investigated: How well does the intelligent, interactive computer-based program with just-in-time feedback enhance preservice teachers’ CK and PCK? and What is the impact of the intelligent, interactive computer-based program with just-in-time feedback on preservice teachers’ teaching of ratios and proportional relationships? An Advisory Board of experts will provide independent feedback throughout the three phases of the project. The final version of the program will be made freely available online. The NSF IUSE: EDU 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 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.