Advertisement

Automatic Trap Detection of Ubiquitous Learning on SCORM Sequencing

  • Chun-Chia Wang
  • H. W. Lin
  • Timothy K. Shih
  • Wonjun Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4159)

Abstract

In order to adapt the teaching in accordance to individual students’ abilities in the distance learning environment, more research emphasis on constructing personalized courseware. The new version of SCORM 1.3 attempts to add the sequence concept into this course standard. The sequencing describes how the sequencing process is invoked, what occurs during the sequencing process and the potential outputs of the sequencing process. However, the related research of sequence trap is lack. Sequence trap results from improper sequence composing. The more complex course is the higher trap-probability arises. When the sequence trap occurs, it will block any learning activities and cannot go on any course object. As a result, we apply the valuable features of Petri net to decrease the complexity of the sequencing definition model in the SCORM 1.3 specification and process the input sequencing information to detect the sequencing trap in advance.

Keywords

Parent Activity Sequencing Control Valid Target Sequencing Rule Sequencing Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    ADL Technical Team: Sharable Content Object Reference Model (SCORM 2004) Documentation, Advanced Distributed Learning (ADL), 1st edn., (January 30, 2004) Google Scholar
  2. 2.
    IMS Simple Sequencing Specification: IMS Global Learning Consortium, Inc. (March 2003), http://www.imsglobal.org
  3. 3.
    Reload Editor (Reload): Reload project (2004), http://www.reload.ac.uk
  4. 4.
    Yang, J.T.D., Tsai, C.Y., Wu, T.H.: Visualized online simple sequencing authoring tool for SCORM-compliant content package. In: Proceedings of the 4th IEEE International Conference on Advanced Learning technologies (ICALT 2004), Finland (August 2004)Google Scholar
  5. 5.
    Shih, T.K., Chang, W.-C., Ko, W.-C.: SCORM Sequence and Template Authoring System. In: Information Resources Management Association International Conference, New Orleans, Louisiana, USA, May 23-26 (2004)Google Scholar
  6. 6.
    Lin, H.W., Shih, T.K., Chang, W.-C., Wang, C.-C.: A Petri Nets-based Approach to Modeling SCORM Sequence. In: Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME 2004), Taipei, Taiwan, June 27-30, pp. 1247–1250 (2004)Google Scholar
  7. 7.
    Su, J.-M., Tseng, S.-S., Chen, C.-Y., Weng, J.-F., Tsai, W.-N.: Constructing SCORM Compliant Course Based on High Level Petri Nets. International Journal Computer Standards & Interfaces (2005)Google Scholar
  8. 8.
    Shih, T.K., Chang, H.-P., Wang, C.-C., Wang, T.-H., Jan, K.H.: SCORM Sequencing Testing. In: International Conference on SCORM, Taipei, Taiwan, January 16-19, pp. 36–39 (2006)Google Scholar
  9. 9.
    ADL Technical Team: SCORM S&N Version 1.3, Advanced Distributed Learning (January 30, 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chun-Chia Wang
    • 1
  • H. W. Lin
    • 2
  • Timothy K. Shih
    • 2
  • Wonjun Lee
    • 3
  1. 1.Department of Information ManagementNorthern Taiwan Institute of Science and Technology, PeitouTaipeiTaiwan, R.O.C.
  2. 2.Department of Computer Science and Information EngineeringTamkang UniversityTamsuiTaiwan, R.O.C.
  3. 3.Department of Computer Science and EngineeringKorea UniversitySeoulRepublic of Korea

Personalised recommendations