Collaborative Research: EAGER: Developing and Optimizing Reflection-Informed STEM Learning and Instruction by Integrating Learning Technologies with Natural Language Processing

合作研究：EAGER：通过将学习技术与自然语言处理相结合来开发和优化基于反思的 STEM 学习和教学

• 批准号: 2329274
• 负责人: Diane Litman
• 金额: $ 10.34万
• 依托单位: University of Pittsburgh
• 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: 2023-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329274&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; Developing; Optimizing

This project aims to enhance student learning and engagement in large lecture STEM courses by developing, optimizing, and evaluating a digital learning environment called CourseMIRROR. CourseMIRROR uses Natural Language Processing (NLP) algorithms and techniques to prompt and scaffold students to create in-depth reflections on their learning experiences. By closely working with a socially and culturally diverse group of students and instructors in public universities and community colleges, the project will directly affect hundreds of students through evidence-based pedagogies and the way educators provide opportunities for learning and engagement. Since we purposefully selected to work with diverse students across institutions, findings will be generalizable to the college student population. Also, the multidisciplinary nature of the project team and work ensures that our results will be reached across traditional disciplinary silos, generating impact in multiple fields, including NLP, Artificial Intelligence (AI), Human-Computer Interaction (HCI), learning sciences, and STEM education. By examining students’ learning through purposeful reflection and feedback loops, this work has the potential to provide a route to personalized learning with innovative approaches to problems vital in the increasingly global economy, thereby opening an important new direction of research in learning sciences and emerging technologies.The proposed project will explore the role of the reflection-informed learning and instruction (RILI) model on students’ engagement and learning outcomes in large lecture STEM courses. The research team will develop and optimize the CourseMIRROR digital learning system that leverages NLP techniques to prompt and scaffold students to write detailed reflections and generate reflection summaries for each lecture. Specifically, this project will incorporate three lines of research: 1) the role of the RILI model on students’ motivation, emotions, and learning, 2) the effectiveness of NLP in creating personalized learning experiences, summarizing reflections in a meaningful way, and evaluating the quality of reflections, and 3) value and design of digital learning tools to improve students’ engagement and learning. This project leverages NLP and HCI techniques and connects them with the RILI model. The aim of combining these approaches emerges to support the innovative and unconventional approach to research, pedagogical strategies, and improved student outcomes. How students learn through iterative cycles of critical reflection and how to effectively utilize and optimize prompts and feedback is not yet well understood or studied. Equally important is how instructors use the process of reflective practice to inform and transform instruction. This project is novel in this respect, as researchers have yet to conduct studies in which these questions are jointly explored and help us explore how learning and engagement can be enabled, improved, and supported across different classes using digital tools, social interactions, and practices.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.

该项目旨在通过开发、优化和评估名为CourseMIRROR的数字学习环境，提高学生学习和参与大型讲座STEM课程的能力。CourseMIRROR使用自然语言处理(NLP)算法和技术来提示和支撑学生对他们的学习经历进行深入的反思。通过与社会和文化上不同的公立大学和社区学院的学生和教师密切合作，该项目将通过循证教学和教育工作者提供学习和参与的机会的方式，直接影响数百名学生。由于我们特意选择了与不同院校的不同学生合作，因此研究结果将适用于大学生群体。此外，项目团队和工作的多学科性质确保了我们的成果将跨越传统的学科孤岛，在多个领域产生影响，包括NLP、人工智能(AI)、人机交互(HCI)、学习科学和STEM教育。通过有目的的反思和反馈循环考察学生的学习，这项工作有可能为个性化学习提供一条途径，以创新的方法解决日益全球化的经济中至关重要的问题，从而开辟学习科学和新兴技术研究的重要新方向。拟议的项目将探索反思知情的学习和教学(RILI)模式在大型讲座STEM课程中对学生参与和学习结果的作用。研究小组将开发和优化CourseMIRROR数字学习系统，该系统利用NLP技术来提示和支撑学生撰写详细的反思并为每堂课生成反思摘要。具体地说，本项目将包括三个方面的研究：1)RILI模型对学生动机、情绪和学习的作用，2)NLP在创造个性化学习体验、以有意义的方式总结反思和评估反思质量方面的有效性，以及3)数字学习工具的价值和设计，以提高学生的参与度和学习。该项目利用了NLP和HCI技术，并将它们与RILI模型联系起来。将这些方法结合起来的目的是为了支持创新和非传统的研究方法、教学战略和改善学生结果。学生如何通过批判性反思的迭代循环学习，以及如何有效地利用和优化提示和反馈，目前还没有得到很好的理解或研究。同样重要的是，教师如何利用反思性实践的过程来告知和改变教学。这个项目在这方面是新颖的，因为研究人员还没有进行研究，共同探索这些问题，并帮助我们探索如何使用数字工具、社交互动和实践在不同的班级启用、改进和支持学习和参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。