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Title:An intelligent tool for annotating collections of written feedback
Author(s):Schoening, Mia Johanna
Advisor(s):Bailey, Brian P
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Content Annotation
Feedback
Intelligent Assistance
Abstract:Online content annotation tools have become increasingly popular as more and more content is being presented in a digital, written format. Yet such tools are still needed in the domain of creative feedback, where annotation is critical in helping users to effectively interpret feedback and make quality revisions. We therefore present a novel content annotation tool (CATIA) aimed at supporting users in annotating collections of written feedback through categorization. Because the process of categorizing feedback is one that is often tedious and time-consuming, our tool integrates intelligent assistance in the form of recommendations, to further assist people as they annotate feedback documents. Through a small study (N = 4) evaluating CATIA against a baseline tool without intelligent assistance, we found that all participants preferred the use of CATIA to complete feedback categorization tasks. The recommendations were shown to be effective at clustering similar statements within a feedback document, thereby reducing the frequency with which users must switch focus between categories. Participants thus responded positively to the integration of the intelligent assistance, crediting the recommendations with making the annotation process easier and less overwhelming overall.
Issue Date:2020-07-21
Type:Thesis
URI:http://hdl.handle.net/2142/108634
Rights Information:Copyright 2020 Mia Schoening
Date Available in IDEALS:2020-10-07
Date Deposited:2020-08


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