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

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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.