Effective Interfaces for Student-Driven Revision Sessions for Argumentative Writing

TL;DR

This paper introduces a web-based intelligent writing assistant for argumentative essays, comparing different interface designs and feedback levels to determine the most effective support for student revisions.

Contribution

It presents the design and evaluation of multiple system versions with varied revision analysis units and feedback levels, identifying the most effective interface for student revision support.

Findings

Detailed revision categorization improves writing outcomes

Simple interfaces are easier to use

Feedback level impacts revision effectiveness

Abstract

We present the design and evaluation of a web-based intelligent writing assistant that helps students recognize their revisions of argumentative essays. To understand how our revision assistant can best support students, we have implemented four versions of our system with differences in the unit span (sentence versus sub-sentence) of revision analysis and the level of feedback provided (none, binary, or detailed revision purpose categorization). We first discuss the design decisions behind relevant components of the system, then analyze the efficacy of the different versions through a Wizard of Oz study with university students. Our results show that while a simple interface with no revision feedback is easier to use, an interface that provides a detailed categorization of sentence-level revisions is the most helpful based on user survey data, as well as the most effective based on improvement in writing outcomes.