Creating MAGIC: System for generating learning object metadata for instructional content

Abstract

This paper presents our latest work on building a system called MAGIC (Metadata Automated Generation for Instructional Content) that will automatically identify segments and generate critical metadata conforming with the SCORM (Sharable Content Object Reference Model) standard for instructional content. Various content analytics engines are utilized to automatically generate key metadata, which include audiovisual analysis modules that recognize semantic sound categories and identify narrators and informative text segments; text analysis modules that extract title, keywords and summary from text documents; and a text categorizer that classifies a document according to a pre-generated taxonomy. With MAGIC, instructional content developers can generate and edit SCORM metadata to richly describe their content asset for use in distributed learning applications. Experimental results obtained from collections of real data from targeted user communities will be presented. Copyright © 2005 ACM.