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    ASIA unversity > 資訊學院 > 資訊工程學系 > 會議論文 >  Item 310904400/7533


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/7533


    Title: Automatic Trap Detection of Ubiquitous Learning on SCORM Sequencing
    Authors: Chun-Chia Wang;H. W. Lin;Timothy K. Shih;Wonjun Lee
    Date: 2006-09
    Issue Date: 2010-01-29 15:56:01 (UTC+8)
    Publisher: Asia University
    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.
    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.
    Relation: The 3rd International Conference on Ubiquitous Intelligence and Computing (UIC-06), China, September 3-6, 2006.4159:1164-1173
    Appears in Collections:[資訊工程學系] 會議論文

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