Show simple item record

dc.contributor.authorWang, Nai-Chingen_US
dc.date.accessioned2019-02-05T09:01:08Z
dc.date.available2019-02-05T09:01:08Z
dc.date.issued2019-02-04
dc.identifier.othervt_gsexam:18689en_US
dc.identifier.urihttp://hdl.handle.net/10919/87437
dc.description.abstractHistorians, like many types of scholars, are often researchers and educators, and both roles involve significant interaction with primary sources. Primary sources are not only direct evidence for historical arguments but also important materials for teaching historical thinking skills to students in classrooms, and engaging the broader public. However, finding high quality primary sources that are relevant to a historian's specialized topics of interest remains a significant challenge. Automated approaches to text analysis struggle to provide relevant results for these "long tail" searches with long semantic distances from the source material. Consequently, historians are often frustrated at spending so much time on manually the relevance of the contents of these archives other than writing and analysis. To overcome these challenges, my dissertation explores the use of crowdsourcing to support historians in analysis of primary sources. In four studies, I first proposed a class-sourcing model where historians outsource historical analysis to students as a teaching method and students learn historical thinking and gain authentic research experience while doing these analysis tasks. Incite, a realization of this model, deployed in 15 classrooms with positive feedback. Second, I expanded the class-sourcing model to a broader audience, novice (paid) crowds and developedthe Read-agree-predict (RAP) technique to accurately evaluate relevance between primary sources and research topics. Third, I presented a set of design principles for crowdsourcing complex historical documents via the American Soldier project on Zooniverse. Finally, I developed CrowdSCIM to help crowds learn historical thinking and evaluated the tradeoffs between quality, learning and efficiency. The outcomes of the studies provide systems, techniques and design guidelines to 1) support historians in their research and teaching practices, 2) help crowd workers learn historical thinking and 3) suggest implications for the design of future crowdsourcing systems.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectCrowdsourcingen_US
dc.subjectHistorical Researchen_US
dc.subjectHistory Educationen_US
dc.titleSupporting Historical Research and Education with Crowdsourced Analysis of Primary Sourcesen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairLuther, Kurten_US
dc.contributor.committeememberLease, Matthewen_US
dc.contributor.committeememberQuigley, Paulen_US
dc.contributor.committeememberFox, Edward A.en_US
dc.contributor.committeememberWang, Gangen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record