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

智能、自适应计划，为职前教师提供及时反馈

• 批准号: 2234015
• 负责人: Yasemin Copur-Gencturk
• 金额: $ 199.97万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234015&HistoricalAwards=false
• 关键词: Intelligent; Adaptive; Program; Just; time

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.

该项目旨在通过一个具有个性化反馈的互动项目，改善职前教师的循证教学实践，为国家利益服务。教师对数学概念的理解以及他们对学生如何学习的理解，以及他们对教学工具和实践的理解，对于实施教学实践至关重要，这些教学实践将引导学生发展对概念的掌握。然而，职前教师可能有不同程度的学习机会，这取决于他们的位置或他们注册的教师培训计划。该项目将通过一个项目来解决这一问题，该项目将在任何地方、任何时间向小学和初中职前教师开放，重点是教授比率和比例关系，这是学习者一直面临的挑战。本研究的基础是采用一种能有效改善在职教师数学教学和学生数学学习的原型。培养一支具有特定内容知识和教学技能的教学队伍，可以为学生创造掌握STEM概念的学习环境。该项目希望促进对职前教师学习的理解，并为使用人工智能的交互式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，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已被确定为教师实施教学实践的必要条件，从而引导学生发展深刻的概念知识。尽管已经发现该计划的原型在改善在职教师的实践和学生的学习方面是有效的，但如何为在职教师设计类似的计划的问题尚未得到研究，职前教师在知识和经验方面与在职教师不同。自然语言处理在多大程度上可以可靠地检测职前教师的反应也将在该项目中进行研究。该计划将根据数学方法课程讲师和职前教师的反馈，通过交互式设计周期进行开发和完善。该项目对职前教师知识和教学实践的有效性将通过一项随机对照试验进行检验，该试验将从全国至少8所大学抽取200名职前教师作为样本。以下问题将被调查：具有即时反馈的智能交互式计算机程序如何提高职前教师的CK和PCK？基于计算机的实时反馈智能交互式程序对职前教师的比例和比例关系教学有何影响？专家咨询委员会将在整个项目的三个阶段提供独立的反馈意见。该计划的最终版本将在网上免费提供。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。通过参与学生学习跟踪，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。