PIs: Rebecca Hwa, Diane Litman
University of Pittsburgh
Natural language processing (NLP) is an integral part of an intelligent tutoring system for writing; it allows the system to automatically analyze student writings and provide feedback to help students to learn. For example, methods have been developed to automatically detect and correct grammar usage errors and to assess aspects of student writing. However, current technology does not offer enough support for teaching students to revise their writings. Unlike mechanical error corrections, the rationales behind revisions are harder to determine. There may be multiple possible changes for an unclear passage in a draft; conversely, one specific writing change might be due to several possible underlying reasons. This EAGER award investigates whether NLP methods can help students to learn to make a more concrete connection between the abstract principles of rewriting (e.g., “A paper should have a clear thesis”) and the particular contexts in which the revision is carried out. The success of this project would enable educational applications that benefit the society.
This project evaluates the viability of revision as a pedagogical technique by determining whether student interactions with the revision assistant enables them to learn to write better — that is, whether certain forms of the feedback (in terms of the perceived purposes and scopes of changes) encourage students to learn to make more effective revisions. More specifically, the project works toward three objectives:
(1) Define a schema for characterizing the types of changes that occur at different levels of the rewriting. For example, the writer might add one or more sentences to provide evidence to support a thesis; or the writer might add just one or two words to make a phrase more precise.
(2) Based on the schema, design a computational model for recognizing the purpose and scope of each change within a revision. One application of such a model is a revision assistant that serves as a sounding board for students as they experiment with different revision alternatives.
(3) Conduct experiments to study the interactions between students and the revision writing environment in which variations of idealized computational models are simulated. The findings of the experiments pave the way for developing better technologies to support for student learning.