No abstract

The Internet is packed with resources which can be used for educational purposes, referred to as Learning Objects (LOs). Locating the LO which is best suited for your educational purposes can be extremely challenging. This since the context surrounding the LO in regards to intended user group, educational level etc. are not included in the resource. The SCORM standard has changed this by including contextual metadata as part of the resource. However, SCORM LOs are scarcely created, much as a result of high knowledge and time requirements needed for creating the necessary metadata. This research has been using Automatic Metadata Generation tools to assist in the metadata creation process, enabling LOs to share common contextual metadata while receiving additional high quality LO specific metadata without the need for manual metadata creation efforts.

Enabling efficient retrieval and re-usage of digital documents is a major challenge as many documents on the Internet and on Intranets are poorly described with metadata. Manual generation of quality metadata requires skilled human resources, is costly and time-consuming. As a result, metadata related to the documents are too often insufficient or even incorrect. Automatic Metadata Generation (AMG) algorithms could perform similar metadata generation efforts in seconds without the need for human efforts. Submission of conference proceedings commonly includes specifying an extensive range of metadata. Conference proceedings are based on a specific document template with strict usage regulations making them a prime candidate for AMG efforts. This paper evaluates usage of AMG to generate metadata from papers based the MS Wordbased IEEE & ACM conference proceedings templates. This enables this research to evaluate if the templates enable efficient AMG efforts, and if the desired paper content is actually retrieved. As authors might not see value in complying with the templates, actual document content can differ from the template specifications.

Giving search engines access to high quality document metadata is crucial for efficient document retrieval efforts on the Internet and on corporate Intranets. Presence of such metadata is currently sparsely present. This paper presents how the structural content of document files can be used for Automatic Metadata Generation (AMG) efforts, basing efforts directly on the documents' content (code) and enabling effective usage of combinations of AMG algorithms for additional harvesting and extraction efforts. This enables usage of AMG efforts to generate high quality metadata in terms of syntax, semantics and pragmatics, from non-homogenous data sources in terms of visual characteristics and language of their intellectual content.

Abstract:The world of closed Learning Management Systems (LMS) is being replaced by open systems for sharing and reusing digital Learning Objects (LOs) between users, courses, institutions and countries. This poses new challenges in describing these LOs with detailed and correct metadata. This information background is needed for querying services to perform accurate queries for LO retrieval. In this paper we present metadata specific challenges when converting from a local LMS with proprietary metadata schema to a global metadata schema. We have uncovered extensive LO description possibilities based on the existing, local LMS, registered metadata, its LO types and the local context. Files can contain extensive metadata descriptions, though require special attention. We have confirmed that technologies developed as crosswalks are valid for usage in this projects for a one-time metadata transferral. However, transferring of all local metadata elements can result in incompatibility issues with other LMSs. This, even when keeping with the global metadata schema.