Argument Graph Supported Multi-Level Approach for Argumentative Writing Assistance
PERIOD:
TO
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 pedagogical 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 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.
The overarching goal for this project is to improve students’ essay writing skills by designing a novel intelligent argumentative writing assistance system, ArguAble, empowered by novel NLP and machine learning methods. Students with varying levels of writing proficiency will use ArguAble in two learning modes: (1) learning with examples, and (2) practice and get feedback, each containing practice opportunities and actionable feedback targeting different argumentation skills.
Primary Investigator(s)
