EXP: Development of Human Language Technologies to Improve Disciplinary Writing and Learning through Self-Regulated Revising

写作和复习是学习的重要部分，然而许多大学生毕业时并没有表现出对学术写作的进步或掌握。这个项目探索了通过一个整合了自然语言处理方法、数据可视化和用户界面方面的最佳实践以及当前教学理论的修订环境来提高学生学术写作的可行性。该环境将支持和鼓励学生发展写作和复习所必需的自我调节技能，包括设定目标、选择写作策略和自我监控进度。作为一种学习技术，该环境可以大规模应用，从而提高包括英语学习者在内的不同学生群体的写作能力。此外，该项目对毕业生的多学科培训侧重于增加网络学习研究和开发的多样性。计划进行三个阶段的调查。首先，为了分析学生修改行为的数据，我们进行了一系列实验来研究学生与修改写作环境变化之间的互动关系。其次，收集的数据形成了开发端到端系统的黄金标准，该系统自动提取学生草稿之间的修改并确定每个修改的目标。考虑了多种抽取算法，包括基于语义相似度的短语对齐和深度学习方法。为了确定修订的目标，根据黄金标准训练有监督的分类器。探索了一组不同的特征和所标识目标的表示(例如，粒度、范围)。除了“提取-然后分类”的流水线外，还开发了一种交替联合序列标记模型。序列的标注用于识别修订目标，并对序列进行突变以生成可能的句子对齐校正以用于修订提取。编写环境被迭代地改进，通过频繁的用户研究来增强界面原型。第三，一个完整的端到端系统，集成了最成功的组件模型，被部署在大学水平的写作课堂上。学生的进度会在多个作业中进行跟踪。

Writing and revising are essential parts of learning, yet many college students graduate withoutdemonstrating improvement or mastery of academic writing. This project explores the feasibilityof improving students' academic writing through a revision environment that integrates naturallanguage processing methods, best practices in data visualization and user interfaces, andcurrent pedagogical theories. The environment will support and encourage students to developself-regulation skills that are necessary for writing and revising, including goal-setting, selectionof writing strategies, and self-monitoring of progress. As a learning technology, the environmentcan be applied on a large scale, thereby improving the writing of diverse student populations,including English learners. Additionally, the project's multidisciplinary training of graduatestudents is focused on increasing diversity in cyberlearning research and development. Three stages of investigation are planned. First, to analyze data on students' revision behaviors,a series of experiments are conducted to study interactions between studentsand variations of the revision writing environment. Second, the collected data forms the goldstandard for developing an end-to-end system that automatically extracts revisions betweenstudent drafts and identifies the goal for each revision. Multiple extraction algorithms areconsidered, including phrasal alignment based on semantic similarity metrics and deep learningapproaches. To identify the goal of a revision, a supervised classifier is trained from the goldstandard. A diverse set of features and the representations of the identified goals (e.g.,granularity, scope) are explored. In addition to the "extract-then-classify" pipeline, an alternativejoint sequence labeling model is also developed. The labeling of sequences is used torecognize revision goals and the sequences are mutated to generate possible corrections ofsentence alignments for revision extraction. The writing environment is iteratively refined,augmenting the interface prototyping through frequent user studies. Third, a completeend-to-end system that integrates the most successful component models is deployed incollege-level writing classes. Student progress is tracked across multiple assignments.