Lindsay Clare Matsumura


2025

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eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback
Zhexiong Liu | Diane Litman | Elaine L Wang | Tianwen Li | Mason Gobat | Lindsay Clare Matsumura | Richard Correnti
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)

The ability to revise essays in response to feedback is important for students’ writing success. An automated writing evaluation (AWE) system that supports students in revising their essays is thus essential. We present eRevise+RF, an enhanced AWE system for assessing student essay revisions (e.g., changes made to an essay to improve its quality in response to essay feedback) and providing revision feedback. We deployed the system with 6 teachers and 406 students across 3 schools in Pennsylvania and Louisiana. The results confirmed its effectiveness in (1) assessing student essays in terms of evidence usage, (2) extracting evidence and reasoning revisions across essays, and (3) determining revision success in responding to feedback. The evaluation also suggested eRevise+RF is a helpful system for young students to improve their argumentative writing skills through revision and formative feedback.

2020

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Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing
Tazin Afrin | Elaine Lin Wang | Diane Litman | Lindsay Clare Matsumura | Richard Correnti
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

Automated writing evaluation systems can improve students’ writing insofar as students attend to the feedback provided and revise their essay drafts in ways aligned with such feedback. Existing research on revision of argumentative writing in such systems, however, has focused on the types of revisions students make (e.g., surface vs. content) rather than the extent to which revisions actually respond to the feedback provided and improve the essay. We introduce an annotation scheme to capture the nature of sentence-level revisions of evidence use and reasoning (the ‘RER’ scheme) and apply it to 5th- and 6th-grade students’ argumentative essays. We show that reliable manual annotation can be achieved and that revision annotations correlate with a holistic assessment of essay improvement in line with the feedback provided. Furthermore, we explore the feasibility of automatically classifying revisions according to our scheme.