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，优化和评估称为Coursemirror的数字学习环境来增强学生的学习和参与度。 Coursemirror使用自然语言处理（NLP）算法和技术来提示和脚手架学生对他们的学习经历进行深入的思考。通过与公立大学和社区大学的社会和文化多样化的学生和讲师密切合作，该项目将通过基于证据的教学法直接影响数百名学生，以及教育工作者为学习和参与提供机会的方式。由于我们有目的地选择与各个机构的潜水员合作，因此发现可以推广到大学生人数。同样，项目团队的多学科性质和工作确保我们的结果将在传统的纪律筒仓中达成，从而在包括NLP，人工智能（AI），人类计算机互动（HCI），学习科学和STEM教育等多个领域产生影响。通过通过有目的的反思和反馈循环来检查学生的学习，这项工作有可能通过在越来越多的全球经济中至关重要的问题来提供个性化学习的途径，从而为学习科学和新兴技术的研究开辟了重要的新方向。拟议的项目将探索反射的学习和教学的作用（RILI）的作用（RILI），并进行了研究（RILI）的培训（RILI），并进行了研究。研究团队将开发和优化Coursemirror数字学习系统，该系统利用NLP技术来提示和脚手架学生写详细的反思并为每个讲座产生反思摘要。具体而言，该项目将结合三条研究：1）RILI模型在学生的动机，情感和学习中的作用，2）NLP在创造个性化学习体验，以有意义的方式总结思考以及评估反思质量以及3）价值和设计工具的价值和设计工具来改善学生的参与和学习的价值和设计。该项目利用NLP和HCI技术并将其与RILI模型联系起来。结合这些方法的目的是为了支持创新和非常规的研究，教学策略和改善学生的成果。学生如何通过批判性反思的迭代循环学习以及如何有效利用和优化提示和反馈尚未得到很好的理解或研究。同样重要的是，讲师如何使用反思练习的过程来告知和转换教学。在这方面，该项目是新颖的，因为研究人员尚未进行共同探索这些问题的研究，并帮助我们探索如何使用数字工具，社交互动和实践来启用，改进和支持各个阶级的学习和支持。该奖项反映了NSF的法规使命，并认为通过基金会的知识优点和广泛的crietia crietia critia critia critia critia criter crietia crietia critia crietia crietia criitia crietia critia critia critia crietia critia critia cripia croperia cragia croperia却反映了奖项。