Argument Graph Supported Multi-Level Approach for Argumentative Writing Assistance

论证图支持多层次的议论文写作辅助方法

• 批准号: 2302564
• 负责人: Lu Wang
• 金额: $ 84.98万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2302564&HistoricalAwards=false
• 关键词: Argument; Graph; Supported; Multi; Level

Proficiency in argumentative writing contributes to one's academic and professional success. However, the Nation's Report Card shows that most adolescents are not skilled in argumentation and frequently experience difficulty when comprehending arguments and constructing well-rounded essays. Traditional teaching approaches for argumentative writing often require students to practice writing a whole essay before receiving feedback, missing deliberate practice opportunities on each difficulty factor that the students experience. On the other hand, while formative and personalized feedback is useful in improving students' logical writing skills, it requires substantive efforts by instructors and causes delays in feedback. This project will generate new insights into artificial intelligence and human-computer interaction capabilities for enhancing student learning of argumentative writing. The proposed research will advance the understanding of how people learn argumentative writing and argumentation. The project will improve the state-of-the-art in natural language processing by developing techniques for argument mining and argument quality measurement. The developed argumentative writing tools can be broadly applicable to many domains, thus providing learning and practice opportunities to anyone who wants to improve their argumentative writing skills. This project will also promote STEM education diversity with a focus on attracting and mentoring women and underrepresented minorities in computer science. The findings, open-source codes, and argumentative writing assistance system will be demonstrated and distributed to the public through various outreach activities at the University of Michigan. Concretely, this project will investigate efficient and scalable pedagogical approaches for argumentative writing. Three main research thrusts will be explored. First, a personalized argumentative writing tutoring system, ARGUABLE, will be built. ARGUABLE is designed with two learning modes: (1) learning with examples, and (2) practicing and getting feedback, each containing practice opportunities and actionable feedback targeting different argumentation skills. Second, novel natural language processing and machine learning models will be investigated to enable multi-level argument understanding and interpretable essay quality measurement. Novel representation learning methods that capture long-distance relations are investigated to extract argument structures more accurately. Graphical representation encoding methods will be used to support feedback provision at multiple levels. A revision suggestion retrieval system will also be built to support novice students with concrete ideas for writing improvement. Finally, evaluations will be conducted in collaboration with instructors who teach argumentative writing at the Ann Arbor and the Dearborn campuses of the University of Michigan, to assess the effectiveness of ARGUABLE.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.

熟练掌握议论文写作有助于一个人在学术和职业上的成功。然而，国家报告卡显示，大多数青少年不具备论证技巧，并且在理解论证和构建全面的论文时经常遇到困难。传统的议论文教学方法通常要求学生在收到反馈之前练习写整篇论文，从而错过了对学生遇到的每个困难因素进行刻意练习的机会。另一方面，虽然形成性和个性化的反馈有助于提高学生的逻辑写作能力，但它需要教师付出实质性的努力，并且会导致反馈的延迟。该项目将对人工智能和人机交互能力产生新的见解，以增强学生议论文写作的学习。拟议的研究将增进对人们如何学习议论文和论证的理解。该项目将通过开发论证挖掘和论证质量测量技术来提高自然语言处理的最新水平。开发的议论文写作工具可以广泛适用于许多领域，从而为任何想要提高议论文写作技巧的人提供学习和练习的机会。该项目还将促进 STEM 教育多样性，重点是吸引和指导计算机科学领域的女性和代表性不足的少数群体。研究结果、开源代码和议论文写作辅助系统将通过密歇根大学的各种外展活动向公众展示和分发。具体来说，该项目将研究有效且可扩展的议论文教学方法。将探讨三个主要研究方向。首先，打造个性化议论文辅导系统ARGUABLE。 ARGUABLE 设计有两种学习模式：(1) 通过示例学习，(2) 练习和获取反馈，每种模式都包含针对不同论证技能的练习机会和可操作的反馈。其次，将研究新颖的自然语言处理和机器学习模型，以实现多层次的论点理解和可解释的论文质量测量。研究了捕获长距离关系的新颖表示学习方法，以更准确地提取论证结构。图形表示编码方法将用于支持多个级别的反馈提供。还将建立修改建议检索系统，为新手提供写作改进的具体想法。最后，将与密歇根大学安娜堡分校和迪尔伯恩校区教授议论文写作的教师合作进行评估，以评估 ARGUABLE 的有效性。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持利用基金会的智力优势和更广泛的影响审查标准。