{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108634"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108634","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"An intelligent tool for annotating collections of written feedback","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Schoening, Mia Johanna"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Bailey, Brian P"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T22:44:42Z","date_published":"2020-10-07T22:44:42Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Content Annotation","Feedback","Intelligent Assistance"],"languages":["en"],"rights":["Copyright 2020 Mia Schoening"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108634","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bailey, Brian P"]},{"key":"dc:creator","label":"Author","values":["Schoening, Mia Johanna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T22:44:42Z","2022-10-07T22:44:53Z","2020-07-21","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Content Annotation","Feedback","Intelligent Assistance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Mia Schoening"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108634"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Online content annotation tools have become increasingly popular as more and more content is being presented in a digital, written format. 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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.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01","The student, Mia Schoening, accepted the attached license on 2020-07-21 at 11:39.","The student, Mia Schoening, submitted this Thesis for approval on 2020-07-21 at 11:57.","This Thesis was approved for publication on 2020-07-21 at 13:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15710 on 2020-10-02 at 15:34:00","Made available in DSpace on 2020-10-07T22:44:42Z (GMT). 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