Automatic trap detection of ubiquitous learning on SCORM sequencing

Chun Chia Wang, H. W. Lin, Timothy K. Shih, Wonjun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationUbiquitous Intelligence and Computing - Third International Conference, UIC 2006, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540380914, 9783540380917
StatePublished - 2006
Event3rd International Conference on Ubiquitous Intelligence and Computing, UIC 2006 - Wuhan, China
Duration: 3 Sep 20066 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4159 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Ubiquitous Intelligence and Computing, UIC 2006


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