Automatic Extraction of Learning Object Metadata (LOM) from HTML Web Pages

  • Authors:

  • Wai Yuen Tang;Lam For Kwok

  • Affiliations:

  • The Department of Computer Science, City University of Hong Kong, wytang@cs.cityu.edu.hk, cslfkwok@cityu.edu.hk;The Department of Computer Science, City University of Hong Kong, wytang@cs.cityu.edu.hk, cslfkwok@cityu.edu.hk

  • Venue:
  • Proceedings of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation
  • Year:
  • 2005

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Abstract

It is difficult to locate learning resources on the Internet due to the loose structure of the web. Even with the help of search engines, there are simply too many search results with poor relevancy. In order to solve this problem, learning technology standards such as ADL SCORM, Dublin Core, IMS Specification, IEEE LOM, etc are emerged to provide a standard to identify and describe learning resources. Learning technology standards provide a structured index in describing learning objects and thus, help users in identifying a learning object with higher relevancy. However, the IEEE LOM standard contains too many attributes, making authors reluctant to use the standard. This paper discusses the difficulties of adapting to learning technology standards and describes a framework that automatically extracts important information from HTML web pages and maps them with attributes of the IEEE LOM standard.