Project Activities
During Phase I in 2022, the team developed a prototype writing platform with preset questionnaires, a mechanism for students to capture and submit a PDF of their paper via a word processor add-on, a digital questionnaire alongside the paper for review, and a reporting dashboard for analysis. At the end of Phase I, a pilot test with 17 high school educators and 407 students demonstrated usability as the platform supported writing exercises and feasibility as the writing exercises were integrated into practice. In addition, 54% of students provided a high-level agreement that the platform supported self-review, and all teachers agreed that the platform saved time and believe the reports can be used to inform feedback and curriculum planning.
In Phase II of the project, the team will fully develop the product, including the backend system and user interface for students and educators, and improvements recommended by teachers in Phase I including question customization, peer review, improved analytics, and machine learning for automated highlighting of text. After development concludes, a pilot study will test the feasibility and usability, fidelity of implementation, and the promise of the product for improving writing. The team will collect data from 40 grade 9 to 11 classes, with half randomly assigned to use the product and the other half to use business-as-usual activities. Researchers will compare pre-and-post scores using the Analytic Writing Continuum for Source-Based Argument (AWC-SBA). Researchers will gather cost information using the "ingredients method" and will include all expenditures on things such as personnel, facilities, equipment, materials, and training.
Products and publications
This Guided Understanding and Assessment of Composition (GUAC) will be an online software system to streamline source-based (e.g., academic) writing assessment by placing prompts and questions next to student-written texts for review by teachers and students. The prompts and questions are organized into teacher-facing and student-facing questionnaires that help teachers evaluate and provide specific, scaffolded feedback to student writers and enable students to reflect on their own drafts and those of peers. A new Machine Learning component will enable automatic essay segmentation to identify and highlight discrete rhetorical and argumentative elements in student writing.
Project website:
Additional project information
Video Demonstration of the Phase I Prototype: https://youtu.be/maxpt256Xbc
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To answer additional questions about this project or provide feedback, please contact the program officer.