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dc.contributor.authorChen, Yinlinen_US
dc.date.accessioned2017-08-29T08:00:26Z
dc.date.available2017-08-29T08:00:26Z
dc.date.issued2017-08-28en_US
dc.identifier.othervt_gsexam:12587en_US
dc.identifier.urihttp://hdl.handle.net/10919/78750
dc.description.abstractEducational Digital Libraries (DLs) are complex information systems which are designed to support individuals' information needs and information seeking behavior. To have a broad impact on the communities in education and to serve for a long period, DLs need to structure and organize the resources in a way that facilitates the dissemination and the reuse of resources. Such a digital library should meet defined quality dimensions in the 5S (Societies, Scenarios, Spaces, Structures, Streams) framework - including completeness, consistency, efficiency, extensibility, and reliability - to ensure that a good quality DL is built. In this research, we addressed both external and internal quality aspects of DLs. For internal qualities, we focused on completeness and consistency of the collection, catalog, and repository. We developed an application pipeline to acquire user-generated computing-related resources from YouTube and SlideShare for an educational DL. We applied machine learning techniques to transfer what we learned from the ACM Digital Library dataset. We built classifiers to catalog resources according to the ACM Computing Classification System from the two new domains that were evaluated using Amazon Mechanical Turk. For external qualities, we focused on efficiency, scalability, and reliability in DL services. We proposed cloud-based designs and applications to ensure and improve these qualities in DL services using cloud computing. The experimental results show that our proposed methods are promising for enhancing and enriching an educational digital library. This work received support from ACM, as well as the National Science Foundation under Grant Numbers DUE-0836940, DUE-0937863, and DUE-0840719, and IMLS LG-71-16-0037-16.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.subjectEducational Digital Libraryen_US
dc.subjectACM Classification Systemen_US
dc.subjectAmazon Mechanical Turken_US
dc.subjectClassificationen_US
dc.subjectTransfer learningen_US
dc.subjectActive learningen_US
dc.subjectYouTubeen_US
dc.subjectSlideShareen_US
dc.subjectDigital Library Service Qualityen_US
dc.subjectCloud Computingen_US
dc.titleA High-quality Digital Library Supporting Computing Education: The Ensemble Approachen_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.committeechairFox, Edward Alanen_US
dc.contributor.committeememberNorth, Christopher L.en_US
dc.contributor.committeememberDas, Sanmayen_US
dc.contributor.committeememberFan, Weiguoen_US
dc.contributor.committeememberTorres, Ricardo Da Silvaen_US


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