With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative...With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative scenario for participants. However, IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper, a Business Process Execution Language （BPEL） enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning （CBR） method. By providing expandable secure learning sequences flexibly, it satisfies the different actual demands for personalize learning展开更多
基金National Natural Science Foundation of China (No.60673010)Natural Science Foundation of Hubei Province ofChina (No.2009CDA135)
文摘With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative scenario for participants. However, IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper, a Business Process Execution Language （BPEL） enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning （CBR） method. By providing expandable secure learning sequences flexibly, it satisfies the different actual demands for personalize learning
