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

该项目旨在通过设计和实现一个人工智能(AI)增强的形成性评估和反馈系统来服务于国家利益。该系统将帮助学生发展基于来源的STEM论点，例如STEM文本摘要，或问题空间，它们是问题和解决问题的多种途径的心理表征。项目的实施将在一所城市大学的大型本科生物理入门课程中进行，该大学服务于不同的和历史上代表性不足的学生群体。学生对课堂前学习活动的持续参与是学生在STEM入门课程中取得成功的关键。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景有一个扎实的理解，以便为课堂上的积极和协作学习做好准备。然而，在以学生为中心的学习模式中，无人监督的课堂前学习可能是一个持续的问题。这一问题在大型初级STEM课程中尤为明显，在这些课程中，传统的教学方法效果较差。该项目的创新将包括人工智能生成的自适应脚手架信息和学习进度反馈，并使用数据可视化技术帮助学生进行概念学习和自我调节行为。由人工智能脚手架反馈系统支持的独特学习机会将显著提高学生在自定进度的在线课堂前学习中的参与度。反过来，这将帮助学生获得内容知识并建立对问题的正确理解，为他们在课堂互动解决问题活动中取得成功做好准备。这个项目的工作将分为三个阶段。首先，项目团队将采取参与式研究(PR)的方法，强调教授物理课程的教员直接参与设计和实施新作业。这些教员还将通过与研究人员的合作来共同构建研究，以在课程中对教师和学生进行混合方法研究。在第一阶段，主要的研究目标是找出利用人工智能支架式课堂前学习的主题和问题，并调查学习者在课前作业中的参与度和进步。在该项目的第二阶段，评估研究将证明课堂前学习中的知识发展是否可以帮助学生解决课堂上的认知要求任务，并在STEM中发展积极的自我效能感。这些发现还将决定人工智能在教育中是否会改善学生在教室内外的幸福感，重点是在STEM教育中传统上代表性不足的学生。在最后阶段收集的大量数据将揭示课堂前活动、课堂表现、自我效能、对物理的兴趣和学生背景之间的关系，包括性别、种族、民族、第一代地位和英语学习。序列挖掘和聚类分析有望揭示学生不同的隐藏参与状态，并对他们的参与轨迹进行分组，解释集群成员资格和轨迹如何因学生背景而异。因此，该项目将为进一步研究开发一种以人工智能为支架的课堂前学习模式奠定基础，该模式将促进大多数学生在物理入门课程中取得成功。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。通过其参与的学生学习跟踪，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 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.