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、优化名为 CourseMIRROR 的数字学习环境来增强学生对大型讲座 STEM 课程的学习和参与度。 CourseMIRROR 使用自然语言处理 (NLP) 算法和技术来提示和支持学生对 STEM 课程进行深入思考。通过与公立大学和社区学院的社会和文化多元化的学生和教师群体密切合作，该项目将通过基于证据的教学法以及教育工作者提供学习和学习机会的方式直接影响数百名学生。由于我们有目的地选择与跨机构的不同学生合作，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领域，从而产生影响。这项工作涵盖 NLP、人工智能 (AI)、人机交互 (HCI)、学习科学和 STEM 教育等多个领域，通过有目的的反思和反馈循环来检查学生的学习情况，有可能为个性化提供一条途径。学习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的重要新研究方向。拟议的项目将探讨反思型学习和教学（RILI）模型对学生参与的作用研究团队将开发和优化 CourseMIRROR 数字学习系统，利用 NLP 技术提示和支撑学生为每堂课撰写详细的反思并生成反思总结。具体而言，该项目将包含三行内容。研究范围： 1) RILI 模型对学生动机、情感和学习的作用，2) NLP 在创造个性化学习体验、以有意义的方式总结反思以及评估反思质量方面的有效性，以及 3) 价值和设计该项目利用 NLP 和 HCI 技术并将其与 RILI 模型相结合，旨在支持创新和非常规的研究方法。学生如何通过批判性反思的迭代循环进行学习以及如何有效利用和优化提示和反馈同样重要的是教师如何利用反思实践过程来提供信息和进行转变。这个项目在这方面是新颖的，因为研究人员尚未进行共同探讨这些问题的研究，并帮助我们探索如何使用数字工具、社交互动、跨班级实现、改进和支持学习和参与。和实践。该奖项反映了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，NSF 的法定使命被认为值得支持。