EAGER: Collaborative Research: Automated Instruction Assistant for Argumentative Essays

EAGER：协作研究：议论文自动教学助手

• 批准号: 1847842
• 负责人: Rebecca Passonneau
• 金额: $ 15.7万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1847842&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Automated; Instruction

Development of students' writing skills promotes critical thinking across disciplines, and professional success. Yet the past decade of the Nation's Report Card on students' writing points to a long-standing crisis in writing instruction that persists through post-secondary school. Students need more instruction on how to write, and instructors need more support to provide students with comprehensive, targeted feedback. This project will develop an Automated Instruction Assistant (AIA) to provide post-secondary instructors with feedback on essays while they grade them, through the application of natural language processing and machine learning techniques to the analysis of essay content and argumentation. The project will apply an iterative design process to a sequence of two argumentative essay assignments in the context of a freshman course on academic skills in a computer science department, where the enrollment is in the hundreds. It will integrate state-of-the art methods in content analysis and argument mining of text to model text meaning more deeply than in previous work. Automated support for application of educational rubrics to argumentative essays will help instructors to provide more comprehensive, standardized feedback for students, and foster transparent and supportive writing instruction. This project will develop an AIA that assigns both a total score to an essay, and individual score dimensions, such as how well an argumentative essay articulates a major claim. The scores will be supported by pointers into the text that provide score justification. As a result, the AIA output can be linked directly to a rubric used by the instructor, which facilitates instructor reflection, training for graders, and feedback for students. The technology will integrate and extend the researchers' previous work on content analysis and argument mining. The content analysis will take as input a small number of reference essays to generate a model of the ideas (propositions) in the domain, weighted by importance within and across essays. The argument mining will identify the propositions that play a role in an argument, and their argument relations. It will produce an argument graph in which the nodes are propositions and edges are argument relations. Integration with the content analysis will ground the propositions in the domain ideas, and make it possible to exploit the role of importance of ideas in the domain, and their prominence within an essay. Methods will include novel applications of dynamic programming and integer linear programming combined with deep learning of rich, low dimensional semantic vectors.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.

培养学生的写作技巧可以促进跨学科的批判性思维和专业上的成功。然而，过去十年的全国学生写作报告卡表明，写作教学长期存在危机，这种危机一直持续到中学后。学生需要更多关于如何写作的指导，教师需要更多的支持来为学生提供全面、有针对性的反馈。该项目将开发一个自动教学助理（AIA），通过应用自然语言处理和机器学习技术来分析论文内容和论证，为大专教师提供论文评分时的反馈。该项目将应用迭代设计过程，在计算机科学系的一门新生学术技能课程的背景下，对两篇议论文作业进行排序，该课程有数百名学生。它将整合最先进的文本内容分析和论据挖掘方法，比以前的工作更深入地建模文本含义。在议论文中应用教育规则的自动化支持将有助于教师为学生提供更全面、标准化的反馈，并促进透明和支持性的写作指导。该项目将开发一个AIA，该AIA将为一篇文章分配总分和个人得分维度，例如一篇议论文阐明主要主张的程度。分数将由文本中提供分数证明的指针来支持。因此，AIA的输出可以直接与教师使用的标准相关联，这有利于教师的反思、对评分者的培训和对学生的反馈。该技术将整合和扩展研究人员之前在内容分析和论据挖掘方面的工作。内容分析将以少量参考文章作为输入，生成该领域的思想（命题）模型，根据文章内部和论文之间的重要性进行加权。论证挖掘将识别在论证中起作用的命题，以及它们的论证关系。它将生成一个参数图，其中节点是命题，边是参数关系。与内容分析的整合将使命题在领域思想中扎根，并使其有可能利用领域中思想的重要性的作用，以及它们在文章中的突出地位。方法将包括动态规划和整数线性规划的新应用，并结合丰富的低维语义向量的深度学习。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。

项目成果

Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays

基于来源的论证论文中内容和论证的标题可靠性和注释

• DOI: 10.18653/v1/w19-4452
• 发表时间: 2019
• 期刊: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
• 作者: Gao, Yanjun;Driban, Alex;Xavier McManus, Brennan;Musi, Elena;Davies, Patricia;Muresan, Smaranda;Passonneau, Rebecca J.
• 通讯作者: Passonneau, Rebecca J.

Automated Pyramid Summarization Evaluation

• DOI: 10.18653/v1/k19-1038
• 发表时间: 2019-11
• 作者: Yanjun Gao;Chen Sun;R. Passonneau