Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Method of Clustering for SCOs in the SCORM

SCORM에서 SCO의 클러스터링 기법

  • 윤홍원 (신라대학교 컴퓨터정보학부)
  • Published : 2006.12.30
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Abstract

A SCO is a learning resource that is retrieved by a learner in the SCORM. A storage policy is required a learner to search SCOs rapidly in e-learning environment. In this paper, We define the mathematical formulation of clustering method for SCOs. Also we present criteria for cluster evaluation and describe procedure to evaluate each SCO. We show the search based on proposed clustering method increase performance than the existing search though performance evaluation.

SCORM에서 SCO는. 학습자가 검색하는 학습 단위가 된다. e-러닝 환경에서 학습자가 찾는 SCO를 신속하게 검색할 수 있는 저장 방법이 필요하다. 본 논문에서는 SCO의 클러스터링 방법을 수학적으로 정형화하여 정의하였다. 또한 SCO를 평가하는 기준을 제시하였고 각 SCO를 평가하는 절차를 나타내었다. 실험을 통하여 제안한 클러스터링 방법에 기반을 둔 검색이 기존의 검색 방법보다 성능이 우수함을 보였다.

Keywords

References

  1. SCORM 2004 2nd Edition Overview, p.21, 2004. 07
  2. SCORM Content Aggregation Model, Version 1.3.1 p.11, 2004.07
  3. SCORM Sequencing and Navigation, Version 1.3.1 p.3, 2004.07
  4. Gennaro Cstagliola, Filomenna Ferrucci, Vittorio Fuccella, 'Web System Architectures: SCORM Run-time Environment as a Service, Proceedings of the 6th International Conference on Web Engineering '06,' p.103, 2006. 07
  5. Makoto Iwayama, Takenobu Tokunaga, 'Cluster-based Text Categorization: A Comparison of Category Search Strategies,' Proceedings of ACM SIGIR'95, pp.273-278, 1995.07
  6. Fabrizio Sebastiani, 'Machine Learning in Automated Text Categorization,' ACM Computing Surveys, Volume 34 No.1, pp.1-47, 2003.03
  7. Ron Bekkerman, Ran EI-Yaniv, Naftali Tishby, Yoad Winter, 'On Feature Distributional Clustering for Text Categorization,' Proceedings of the 24th ACM SIGIR, pp.146-153, 2001. 09
  8. SCORM Run-Time Environment, Version 1.3.1 p.3-8, 2004.07
  9. Xuanhui Wang, Jian-Tao Sun, Zhen Chen, ChenXiang Zhai, 'Machine Learning: Latent Semantic Analysis for Multiple-type Interrelated Data Objects,' Proceedings of the 29th ACM SIGIR'06, pp.236-243, 2006. 08
  10. R. Baeza-Yate, B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 1999
  11. D. Simoes, R. Luis, N. Horta, 'Enhancing the SCORM Metadata Model,' Proceedings of the 13th WWW Conference, pp.238-239, 2004. 05