Artificial Intelligence-Scaffolded Pre-Classroom Learning for Large, Introductory Undergraduate Physics Courses

人工智能——大型本科物理入门课程的支架式课前学习

• 批准号: 2315709
• 负责人: Min Kyu Kim
• 金额: $ 29.84万
• 依托单位: Georgia State University Research Foundation, Inc.
• 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 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315709&HistoricalAwards=false
• 关键词: Artificial; Intelligence; Scaffolded; Pre; Classroom

This project aims to serve the national interest by designing and implementing an Artificial Intelligence (AI)-augmented formative assessment and feedback system. This system will help students develop source-based STEM arguments, such as STEM text summarization, or problem spaces, which are mental representations of a problem and of multiple paths to solving it. Project implementation will take place in large, undergraduate introductory physics courses at an urban university that serves diverse and historically underrepresented student groups. Persistent learner engagement in pre-classroom learning activities is critical to learner success in introductory STEM courses. Undergraduate students often need to develop a solid understanding of content or problem situations in self-paced online learning contexts to prepare for in-classroom active and collaborative learning. However, unsupervised pre-classroom learning can be an ongoing issue in a student-centered learning model. This problematic situation is particularly evident in large introductory-level STEM courses where traditional instructional techniques are less effective. The innovation of this project will include AI-generated adaptive scaffolding information and learning progress feedback with data visualization techniques to help students with conceptual learning and self-regulatory behaviors. The unique learning opportunities supported by an AI-scaffolded feedback system will significantly increase students' engagement levels in self-paced online pre-classroom learning. This, in turn, should help students acquire content knowledge and build a proper understanding of problems to prepare themselves for success with in-classroom interactive problem-solving activities.Three phases will govern the work of this project. First, the project team will take a Participatory Research (PR) approach that emphasizes the direct engagement of faculty members who teach physics courses in designing and implementing new assignments. These faculty members will also co-construct research through a partnership with researchers to conduct a mixed-methods study of instructors and students in the courses. During this first phase the primary research goal is to identify topics and problems that utilize AI-scaffolded pre-classroom learning and investigate learner engagement and progression in the pre-class assignments. In the project's second phase evaluation studies will demonstrate whether knowledge development during pre-classroom learning can help students solve cognitively demanding tasks in classrooms and develop positive self-efficacy in STEM. The findings will also determine whether AI in education improves students' well-being inside and outside of classrooms, with a focus on students traditionally underrepresented in STEM education. Extensive data collected in the final phase will uncover the relationships among pre-classroom activities, in-classroom performance, self-efficacy, interest in physics, and student backgrounds, including gender, race, ethnicity, first-generation status, and English language learning. The sequence mining and cluster analysis are expected to reveal students' different hidden engagement states and group their engagement trajectories, explaining how cluster membership and trajectories vary across students' backgrounds. Consequently, this project will lay the groundwork for further research to develop an AI-scaffolded pre-classroom learning model that promotes most students' success in introductory physics courses. 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.

该项目旨在通过设计和实施人工智能（AI）授权的形成性评估和反馈系统来服务国家利益。该系统将帮助学生开发基于源的STEM论点，例如STEM文本摘要或问题空间，这些论点是问题的心理表示，也是解决该问题的多种途径。项目实施将在一所城市大学的大型本科生介绍性物理课程中进行，该大学为多样化且历史上代表不足的学生群体提供服务。持续的学习者参与课堂前学习活动对学习者在入门STEM课程中的成功至关重要。本科生通常需要在自定进度的在线学习环境中对内容或问题情况有牢固的了解，以准备在课堂上积极和协作学习。但是，在以学生为中心的学习模型中，无监督的教室学习可能是一个持续的问题。在传统教学技术效率较低的大型入门级茎课程中，这种有问题的情况尤其明显。该项目的创新将包括AI生成的适应性脚手架信息和通过数据可视化技术学习进度反馈，以帮助学生具有概念学习和自我调节行为。 AI型反馈系统支持的独特学习机会将显着提高学生在自我评价的在线培训前学习中的参与水平。反过来，这应该有助于学生获得内容知识，并建立对问题的正确理解，以通过教室内交互式问题解决活动为成功做好准备。三个阶段将控制该项目的工作。首先，项目团队将采用参与性研究（PR）方法，该方法强调教授在设计和实施新任务时教物理课程的教师的直接参与。这些教职员工还将通过与研究人员建立合作伙伴关系来共同建设研究，以对课程中的教师和学生进行混合方法研究。在第一阶段，主要的研究目标是确定利用AI障碍的教室学习并研究学习前分配的学习者参与和进步的主题和问题。在项目的第二阶段，评估研究将证明课堂前学习期间的知识发展是否可以帮助学生在课堂上解决认知要求的任务，并在STEM中发展积极的自我效能。这些发现还将确定教育中的AI是否可以改善教室内外的学生的福祉，关注传统上的学生在STEM教育方面的人数不足。在最后阶段收集的大量数据将揭示教室活动，课堂内表现，自我效能感，物理学兴趣和学生背景之间的关系，包括性别，种族，种族，第一代人士和英语语言学习。预计序列挖掘和集群分析将揭示学生不同的隐藏参与状态并将其参与轨迹分组，从而解释了群集成员资格和轨迹如何在学生的背景下变化。因此，该项目将为进一步的研究奠定基础，以开发AI型的培训前学习模型，从而促进大多数学生在入门物理课程中的成功。 NSF IUSE：EDU计划支持研发项目，以提高所有学生STEM教育的有效性。通过其参与的学生学习轨道，该计划支持了有前途的实践和工具的创建，探索和实施。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。