Educational 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. / Ph. D. / Educational Digital Libraries (DLs) are designed to serve users finding educational materials. To have a broad impact on the communities in education for a long period, DLs need to structure and organize the resources in a way that facilitates their dissemination and reuse. Such a digital library should be built on a well-defined framework to ensure that the services it provides are of good quality.
In this research, we focused on the quality aspects of DLs. We developed an application pipeline to acquire resources contributed by the users from YouTube and SlideShare for an educational DL. We applied machine learning techniques to build classifiers in order to catalog DL collections using a uniform classification system: the ACM Computing Classification System. We also used Amazon Mechanical Turk to evaluate the classifier’s prediction result and used the outcome to improve classifier performance. To ensure efficiency, scalability, and reliability in DL services, we proposed cloud-based designs and applications to enhance DL services. The experimental results show that our proposed methods are promising for enhancing and enriching an educational digital library.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/78750 |
| Date | 28 August 2017 |
| Creators | Chen, Yinlin |
| Contributors | Computer Science, Fox, Edward A., North, Christopher L., Das, Sanmay, Fan, Weiguo, Torres, Ricardo da Silva |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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