课题基金 / 基金详情

Collaborative Research: STEM Learning Embedded in a Machine-in-the-Loop Collaborative Story Writing Game

Collaborative Research: STEM Learning Embedded in a Machine-in-the-Loop Collaborative Story Writing Game
协作研究:嵌入机器在环协作故事写作游戏中的 STEM 学习
批准号:
2202506
负责人:
Mohit Iyyer
金额:
$62.14万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-09-01 至 2025-08-31

项目摘要

项目成果

Mohit Iyyer的其他基金

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中文摘要
翻译
培养协作、沟通、批判性思维和创造力(4C)等21世纪技能对于学生跟上未来不断发展的劳动力市场变得越来越重要。有效和高效地教授4C需要将它们与核心内容知识领域深度交织在一起,因为获取领域知识可以促进学生这些软技能的发展。在这个项目中,研究人员向将4C技能发展与STEM教育相结合迈出了一步,开发了一个协作写作游戏,在游戏中,多名学生一起围绕嵌入的STEM教育元素进行叙事。作为一项关键创新,调查人员将在这款协作写作游戏中嵌入自然语言处理和基于人工智能(AI)的工具,以自动化事实核查、反馈、知识追踪和叙事故事弧形建议,这将促进学生在掌握方面的进步,同时减轻教师的工作量。总体而言,该项目有可能提高学生对STEM学习活动的参与度,并改善学习结果。该项目将以合作故事写作平台StoriumEdu为基础,因此通过传播该小组的研究成果,直接受益于其2 000个K-12教室的用户基础,超过27 000名学生,并可能使更多的学生受益。这个项目的主要技术目标是用基于人工智能的工具来加强科学写作教学。为了实现这些目标,该项目将开发自动提供写作辅助和反馈的新技术,这些工具将通过StoriumEdu平台部署到K-12教室,以评估其效果。一个核心的技术挑战是通过建立用于事实核查的机器学习模型来评估学生写作的真实性。该团队建议设计检索增强的神经网络,可以定位学生书写的文本中表现出科学误解的跨度。然后,这些跨度将与教科书或在线文章中的相关段落联系起来,使学生能够轻松纠正错误。在开发了事实核查方法后,该团队还将专注于知识追踪,这可以根据学生已经掌握或仍在努力掌握的概念来衡量他们随着时间的推移取得的进步。将根据科学素养专家的反馈开发知识追溯模型。这些模型的输出为这个项目的最后方面提供了信息,该项目旨在产生与概念误解相关的叙事进展。这将允许学生更强烈地参与到他们尚未掌握的概念中,从而最大限度地发挥写作平台的教学潜力。作为一个整体,该项目的研究贡献将新的NLP方法与教育进度跟踪和反馈系统相结合,以努力改善STEM学习。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。
英文摘要
Developing “21st century skills” such as collaboration, communication, critical thinking, and creativity (the 4Cs) has become increasingly important for students to keep up with the ever-evolving labor market of the future. Teaching the 4Cs effectively and efficiently requires deeply intertwining them with core content knowledge areas, since the acquisition of domain knowledge can bolster students’ development of these soft skills. In this project, the investigators take a step towards combining 4C skill development with STEM education by developing a collaborative writing game in which multiple students work together to craft a narrative around embedded STEM education elements. As a key innovation, the investigators will embed this collaborative writing game with natural language processing and artificial intelligence (AI)-based tools to automate fact-checking, feedback, knowledge tracing, and narrative story arc suggestions, which will facilitate students’ progress toward mastery while reducing teacher workload. Overall, this project has the potential to increase student engagement in STEM learning activities and improve learning outcomes. The project will be grounded in StoriumEdu, a collaborative story writing platform, therefore directly benefiting its user base of 2,000 K-12 classrooms with over 27,000 students and potentially an even larger number of students through the dissemination of the team’s research findings. This major technical goals of this project are intended to augment scientific writing instruction with AI-based tools. To achieve these goals, the project will develop novel technologies that automatically provide writing assistance and feedback, and these tools will be deployed into K-12 classrooms via the StoriumEdu platform in order to evaluate their effectiveness. A core technical challenge is to assess the factuality of student writing by building machine learning models for fact-checking. The team proposes to design retrieval-augmented neural networks that can localize spans within student-written text that exhibit scientific misunderstandings. These spans will then be connected with relevant passages from textbooks or online articles to enable students to easily correct their errors. After developing fact-checking methods, the team will also focus on knowledge tracing, which allows measuring student progress over time in terms of which concepts they have mastered or are still struggling with. The knowledge tracing models will be developed with feedback from scientific literacy experts. The output of these models informs the final aspect of this project, which aims to generate narrative progressions associated with conceptual misunderstandings. This will allow students to engage more strongly with concepts that they have yet to master, which maximizes the writing platform’s pedagogical potential. Taken as a whole, this project’s research contributions synthesize novel NLP methods with educational progress tracking and feedback systems in an effort to improve STEM learning.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.
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI: 10.48550/arxiv.2301.13298
发表时间: 2023-01
期刊: ArXiv
影响因子: --
作者: [Kalpesh Krishna;Erin Bransom;Bailey Kuehl;Mohit Iyyer;Pradeep Dasigi;Arman Cohan;Kyle Lo]
通讯作者: Kalpesh Krishna;Erin Bransom;Bailey Kuehl;Mohit Iyyer;Pradeep Dasigi;Arman Cohan;Kyle Lo
DOI: 10.18653/v1/2022.emnlp-main.254
发表时间: 2022
期刊:
影响因子: --
作者: [Naiming Liu;Zichao Wang]
通讯作者: Naiming Liu;Zichao Wang
DOI: 10.18653/v1/2023.acl-long.181
发表时间: 2023
期刊: Association for Computational Linguistics
影响因子: --
作者: [Xu, Fangyuan, Song, Yixiao, Iyyer, Mohit, Choi, Eunsol]
通讯作者: Choi, Eunsol
Collaborative Research: RI: Medium: Multilingual Long-form QA with Retrieval-Augmented Language Models
  • 批准号:
    2312949
  • 项目类别:
    Standard Grant
  • 资助金额:
    $55.42万
  • 财政年份:
    2023
  • 负责人:
    Mohit Iyyer
  • 依托单位:
CAREER: Building Creative Writing Assistants for Machine-in-the-Loop Storytelling
  • 批准号:
    2046248
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $53.23万
  • 财政年份:
    2021
  • 负责人:
    Mohit Iyyer
  • 依托单位:
RI: Medium: Tree-Structured Self-Supervised Modeling for Natural Language
  • 批准号:
    1955567
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $113.09万
  • 财政年份:
    2020
  • 负责人:
    Mohit Iyyer
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)