The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Supporting Interoperability and Context-Awareness in E-Learning through Situation-Driven Learning Processes
Abstract
Current E-Learning technologies primarily follow a data and metadata-centric paradigm by providing the learner with composite content containing the learning resources and the learning process description, usually based on specific metadata standards such as ADL SCORM or IMS Learning Design. Due to the design-time binding of learning resources, the actual learning context cannot be considered appropriately at runtime, what limits the usability and interoperability of learning resources. This paper proposes Situation-driven Learning Processes (SDLP) which describe learning processes semantically from two perspectives: the user perspective considers a learning process as a course of learning goals which lead from an initial situation to a desired situation, whereas the system perspective utilizes Semantic Web Services (SWS) technology to semantically describe necessary resources for each learning goal within a specific learning situation. Consequently, a learning process is composed dynamically and accomplished in terms of SWS goal achievements by automatically allocating learning resources at runtime. Moreover, metadata standard-independent SDLP are mapped to established standards such as ADL SCORM and IMS LD. As a result, dynamic adaptation to specific learning contexts as well as interoperability across different metadata standards and application environments is achieved. To prove the feasibility, a prototypical application is described finally.
Related Content
|
XiFeng Liao.
© 2024.
19 pages.
|
|
Ahmed Abdulateef Al Khateeb, Tahani I. Aldosemani, Sumayah Abu-Dawood, Sameera Algarni.
© 2024.
16 pages.
|
|
Hao Yang.
© 2024.
17 pages.
|
|
Mohammed Abdullatif Almulla.
© 2024.
26 pages.
|
|
Kyosuke Takami, Brendan Flanagan, Yiling Dai, Hiroaki Ogata.
© 2024.
23 pages.
|
|
Shaobin Chen, Qingrong Li, Tao Wang.
© 2024.
22 pages.
|
|
Yan Zhang.
© 2024.
16 pages.
|
|
|