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.

REFERENCES

• 1: ADL, "SCORM 2004 documentation," Downloadable at http://www.adlnet.org/scorm/history/2004/index.cfm, 2004.
• 2: Brad A. Myers , Juan P. Casares , Scott Stevens , Laura Dabbish , Dan Yocum , Albert Corbett, A multi-view intelligent editor for digital video libraries, Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries, p.106-115, January 2001, Roanoke, Virginia, United States [doi>10.1145/379437.379461]
• 3: Y. Li and C. Dorai, "SVM-based audio classification for instructional video analysis," ICASSP'04, 2004.
• 4: Y. Li and C. Dorai, "Video frame identification for learning media content understanding," ICME, 2005.
• 5: Jae-Chang Shim , Chitra Dorai , Ruud Bolle, Automatic Text Extraction from Video for Content-Based Annotation and Retrieval, Proceedings of the 14th International Conference on Pattern Recognition-Volume 1, p.618, August 16-20, 1998
• 6: IBM, "languageware TM," http://www-306.ibm.com/software/globalization/topics/languageware/design.jsp.
• 7: B. Boguraev and M. Neff, "Lexical cohesion, discourse segmentation and document summarization," RIAO, 2000.
• 8: Charu C. Aggarwal , Stephen C. Gates , Philip S. Yu, On Using Partial Supervision for Text Categorization, IEEE Transactions on Knowledge and Data Engineering, v.16 n.2, p.245-255, February 2004 [doi>10.1109/TKDE.2004.1269601]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

General Terms:
Algorithms, Design, Experimentation

Keywords:
e-learning, instructional video content analysis, learning object metadata, text analysis, text categorization