e R evise+ RF : A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback (2025)

Abstract: 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.

## introduction young student writers often struggle with identifying convincing evidence and connecting it to claims when writing argumentative essays; however, developing persuasive argumentation itself helps students improve their thinking and reasoning skills @xcite . this motivates recent research in developing automated writing evaluation (awe) systems for supporting students in writing and revising argumentative essays. for instance, @xcite developed the erevise system to score student essays on text-based evidence usage and to provide associated feedback to guide revision. although students attempted to respond to the feedback, their revisions often did not yield substantive essay improvement @xcite . similar findings have been found for other awe systems @xcite , suggesting that students often lack the skills necessary for effective revision @xcite . ## related awe work on revision while awe systems typically allow submissions of multiple drafts in response to feedback, most provide feedback on single essay drafts rather than on the revisions between two drafts @xcite @xcite . however, feedback on revisions has been identified as an important area for awe research @xcite @xcite , and some systems have visualized nlp revision analyses, e.g., to assist students in self-monitoring writing @xcite @xcite . while these systems display revisions, they do not provide feedback messages based on an nlp assessment of a revision's success. although prior nlp revision work has created annotated revision corpora @xcite @xcite @xcite and has developed models for isolated tasks like identifying revision purposes @xcite @xcite @xcite , generating revisions @xcite , or evaluating revision quality @xcite , our work is the first to integrate multiple such tasks into a deployed and evaluated awe system. ## system deployment the erevise+rf system was deployed in collaboration with 6 teachers and 406 students in grades 4 to 8 during spring 2024 for the mvp article and fall 2024 for the space article. the teachers overlapped for the 2 deployments, while the students were different (e.g., no grade 6 students in fall). the participants were from one school in pennsylvania (pa), where 35% of the students are students of color, representing 45 different zip code areas, and speaking more than 10 languages at home; two schools in louisiana (la), where 98% and 65% enrollment are minorities, respectively, and student reading proficiency rates range from 46% to 47%. the system was deployed in three sessions administered over 3 to 5 days (see figure 2 ). the two deployments received a total of 194 draft1, 194 draft2, and 172 draft3 for mvp and 176 draft1, 176 draft2, and 150 draft3 for space essays since students might not have consistently participated in all three sessions due to absences. our analysis is based on the essays from students who participated in all three sessions, yielding 3 drafts of 172 mvp essays (516 total) and of 150 space essays (450 total). ## conclusion we present erevise+rf, an advanced version of the erevise system that extends and integrates algorithms to assess argumentative essay revisions, and to provide formative feedback on both evidence usage and revision success in response to feedback. although the algorithms are primarily adapted from our prior published work, their integration into a complete and deployed system is a novel contribution. through our pilot studies, we show promise of using nlp algorithms to scaffold young students in writing and revising text-based argumentative essays. in future work, we will deploy the system in more classrooms and with other rta articles, to generalize our findings.