EAGER: Collaborative Research: Automated Instruction Assistant for Argumentative Essays

学生写作技能的发展促进了跨学科的批判性思维和专业成功。然而，过去十年的国家学生写作成绩单表明，写作教学中长期存在的危机一直持续到高中阶段。学生需要更多关于如何写作的指导，教师需要更多的支持，为学生提供全面的、有针对性的反馈。该项目将开发一个自动化教学助理(AIA)，通过将自然语言处理和机器学习技术应用于分析论文内容和论证，为中学后教师在对论文进行评分时提供对论文的反馈。该项目将在计算机科学系一年级学术技能课程的背景下，将迭代设计过程应用于一系列两篇议论文作业，该系招生人数达数百人。它将结合文本内容分析和论证挖掘的最新方法，比以前的工作更深入地建模文本意义。将教育习语应用于议论文的自动化支持将有助于教师为学生提供更全面、更标准化的反馈，并促进透明和支持性的写作教学。这个项目将开发一个AIA，它既可以给一篇文章分配总分，也可以给出单独的分数维度，比如一篇议论文表达一个主要主张的能力有多强。分数将由指向提供分数理由的文本的指针来支持。因此，AIA的输出可以直接链接到教师使用的评分标准，这有助于教师反思、对评分员进行培训和对学生进行反馈。这项技术将整合和扩展研究人员之前在内容分析和论点挖掘方面的工作。内容分析将以少量参考论文作为输入，以生成领域中想法(命题)的模型，根据论文内部和跨论文的重要性进行加权。论元挖掘将识别在论元中起作用的命题及其论元关系。它将产生一个参数图，其中节点是命题，边是参数关系。与内容分析的结合将为领域思想中的命题奠定基础，并使开发思想在领域中的重要性以及它们在论文中的重要性的作用成为可能。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。

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.