ABSTRACT
With the rise of community-generated web content, the need for automatic characterization of resource quality has grown, particularly in the realm of educational digital libraries. We demonstrate how identifying concrete factors of quality for web-based educational resources can make machine learning approaches to automating quality characterization tractable. Using data from several previous studies of quality, we gathered a set of key dimensions and indicators of quality that were commonly identified by educators. We then performed a mixed-method study of digital library curation experts, showing that our characterization of quality captured the subjective processes used by the experts when assessing resource quality for classroom use. Using key indicators of quality selected from a statistical analysis of our expert study data, we developed a set of annotation guidelines and annotated a corpus of 1000 digital resources for the presence or absence of these key quality indicators. Agreement among annotators was high, and initial machine learning models trained from this corpus were able to identify some indicators of quality with as much as an 18% improvement over the baseline.
- B. T. Adler and L. de Alfaro. A content-driven reputation system for the wikipedia. In Proceedings of the 16th international conference on World Wide Web, pages 261--270, Ban, Alberta, Canada, 2007. ACM. Google ScholarDigital Library
- J. E. Blumenstock. Size matters: Word count as a measure of quality on wikipedia. In Proceedings of the 17th International World Wide Web Conference, pages 1095--1096, New York, NY, USA, 2008. ACM. Google ScholarDigital Library
- T. Carey and G. L. Hanley. Extending the impact of open educational resources through alignment with pedagogical content knowledge and institutional strategy: Lessons learned from the merlot community experience. In Opening up education: the collective advancement of education through open technology, open content, and open knowledge, chapter 12. MIT Press, 2008.Google Scholar
- CLEANEVAL home page. http://cleaneval.sigwac.org.uk/, Oct. 2008.Google Scholar
- Climate change collection. http://serc.carleton.edu/climatechange/, Oct. 2008.Google Scholar
- M. Custard and T. Sumner. Using machine learning to support quality judgments. D-Lib Magazine, 11(10), Oct. 2005.Google ScholarCross Ref
- S. de la Chica. Generating Conceptual Knowledge Representations to Support Students Writing Scientific Explanations. PhD thesis, University of Colorado, 2008.Google Scholar
- H. Devaul, A. Diekema, and J. Ostwald. Computer-assisted assignment of educational standards using natural language processing. Unpublished technical report, Digital Learning Sciences, Boulder, CO, 2007.Google Scholar
- Digital library for earth system education. http://www.dlese.org/, Oct. 2008.Google Scholar
- Digital water education library. http://www.csmate.colostate.edu/DWEL/, Jan. 2004.Google Scholar
- DLESE Community Collection (DCC) scope statement. http://www.dlese.org/Metadata/collections/scopes/dcc-scope.php, Oct. 2008.Google Scholar
- P. Dmitriev. As we may perceive: Finding the boundaries of compound documents on the web. In Proceedings of the 17th International World Wide Web Conference, 2008. Google ScholarDigital Library
- D. F. Dufty, D. Mcnamara, M. Louwerse, Z. Cai, and A. C. Graesser. Automatic evaluation of aspects of document quality. In Proceedings of the 22nd annual international conference on Documentation, 2004. Google ScholarDigital Library
- N. Eiron. Untangling compound documents on the web. In Proceedings of the 14th ACM Conference on Hypertext and Hypermedia, pages 85--94, 2003. Google ScholarDigital Library
- K. A. Ericsson and H. A. Simon. Protocol Analysis: Verbal Reports as Data. The MIT Press, revised edition, Apr. 1993.Google Scholar
- B. J. Fogg, J. Marshall, O. Laraki, A. Osipovich, C. Varma, N. Fang, J. Paul, A. Rangnekar, J. Shon, P. Swani, and M. Treinen. What makes web sites credible?: a report on a large quantitative study. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 61--68, Seattle, Washington, United States, 2001. ACM. Google ScholarDigital Library
- M. Y. Ivory, R. R. Sinha, and M. A. Hearst. Empirically validated web page design metrics. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 53--60, Seattle, Washington, United States, 2001. ACM. Google ScholarDigital Library
- T. Joachims. Making large-scale support vector machine learning practical. In Advances in kernel methods: support vector learning, pages 169--184. MIT Press, 1999. Google ScholarDigital Library
- P. V. Ogren, P. G. Wetzler, and S. Bethard. ClearTK: A UIMA toolkit for statistical natural language processing. In UIMA for NLP workshop at Language Resources and Evaluation Conference (LREC), 2008.Google Scholar
- R. Reitsma, B. Marshall, M. Dalton, and M. Cyr. Exploring educational standard alignment: in search of 'relevance'. In Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, pages 57--65, Pittsburgh PA, PA, USA, 2008. ACM. Google ScholarDigital Library
- S. Y. Rieh. Judgment of information quality and cognitive authority in the web. Journal of the American Society for Information Science and Technology, 53:145--161, 2002. Google ScholarDigital Library
- T. Sumner, M. Khoo, M. Recker, and M. Marlino. Understanding educator perceptions of "quality" in digital libraries. In Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries, pages 269--279, Houston, Texas, 2003. IEEE Computer Society. Google ScholarDigital Library
- H. Zeng, M. Alhossaini, L. Ding, R. Fikes, and D. Mcguinness. Computing trust from revision history. In Proceedings of the 2006 International Conference on Privacy, Security and Trust, Oct. 2006. Google ScholarDigital Library
- X. Zhu and S. Gauch. Incorporating quality metrics in centralized/distributed information retrieval on the world wide web. In SIGIR '00: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 288--295, New York, NY, USA, 2000. ACM. Google ScholarDigital Library
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- Automatically characterizing resource quality for educational digital libraries
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