Argument Graph Supported Multi-Level Approach for Argumentative Writing Assistance

熟练的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，大多数青少年不擅长辩论，在理解论点和构建全面的文章时经常遇到困难。传统的议论文写作教学方法通常要求学生在收到反馈之前练习写一篇完整的文章，在学生经历的每个困难因素上都错过了刻意练习的机会。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技能，但它需要教师付出实质性的努力，并造成反馈的延迟。该项目将对人工智能和人机交互能力产生新的见解，以加强学生的议论文写作学习。提出的研究将促进人们如何学习议论文写作和论证的理解。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能的人提供学习和实践的机会。该项目还将促进STEM教育的多样性，重点是吸引和指导计算机科学领域的女性和未被充分代表的少数族裔。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。具体地说，这个项目将研究议论文写作的有效和可扩展的教学方法。将探讨三个主要的研究重点。首先，建立个性化的议论文写作辅导系统ARGUABLE。ARGUABLE设计了两种学习模式：(1)举例子学习，(2)练习并获得反馈，每种模式都包含针对不同论证技巧的练习机会和可操作的反馈。其次，将研究新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。为了更准确地提取论点结构，研究了一种新的捕获远距离关系的表示学习方法。图形表示编码方法将用于支持多层次的反馈提供。此外，还将建立修订建议检索系统，为初学写作的学生提供具体的改进意见。最后，评估将与密歇根大学安娜堡分校和迪尔伯恩分校的议论文写作教师合作进行，以评估ARGUABLE的有效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.