Automatic Trap Detection: A Debugging Mechanism for Abnormal Specification in the IMS Sequencing Controls

Senior Member, IEEE

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Adaptive learning is a challenging trend of distance education. In order to adapt diversified teaching in accordance with individual abilities, educators emphasize on constructing personalized courseware as an Internet-based application. To cope with the drawback of nonsupervised blind spots on the dynamic navigation of learners, the SCORM 2004 specification adds the concept of sequences (i.e., based on IMS Simple Sequence Specification) into its content delivery. Sequencing rules describe how individual navigation is invoked, what learning objects are delivered during the navigation process, and the potential outputs of the process. However, there is a potential leak from using sequence specification. It is possible for an instruction designer to make a mistake similar to that made by any programmer in software development. In other words, a sequencing trap results from improper sequence composition, which may block learning activities from a normal navigation. Although the problem can be manually checked with an ordinary SCORM presentation tool, nonsystematic debugging can be an unsound solution and a time consuming job, since the complexity of courseware is usually proportional to the occurrences of traps. We point out the sequencing trap problem and its solution by using a fully automatic mechanism for trap detection. A Petri net is used in the computation of sequencing traps. The contribution of this paper can be used as a first technique for automatic sequencing testing in the IMS Simple Sequence Specification and SCORM 2004.

Original languageEnglish
Pages (from-to)176-189
Number of pages14
JournalIEEE Transactions on Learning Technologies
Volume1
Issue number3
DOIs
StatePublished - 2008

Keywords

  • IMS Simple Sequence Specification
  • Petri net
  • SCORM 2004
  • Web-based learning
  • automatic testing
  • distance learning
  • sequence debugging

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