Automatic trap detection of ubiquitous learning on SCORM sequencing

  • Authors:

  • Chun-Chia Wang;H. W. Lin;Timothy K. Shih;Wonjun Lee

  • Affiliations:

  • Department of Information Management, Northern Taiwan Institute of Science and Technology, Peitou, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taiwan, R.O.C.;Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea

  • Venue:
  • UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
  • Year:
  • 2006

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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.