Publication | Closed Access
An extended and adaptable information model for learning objects
10
Citations
9
References
2004
Year
Unknown Venue
Artificial IntelligenceE-learningEngineeringMachine LearningObject CategorizationAdaptable Information ModelEducational InformaticsEducationSemantic WebAdaptive Hypermedia SystemLearning Management SystemInformation ModelInformation RetrievalPattern RecognitionCognitive ScienceLearning ObjectLearning AnalyticsNew Conceptual ModelCourse CompositionAdaptive Learning
Learning objects are fundamental elements of a new conceptual model for content creation and course composition in Web-based education. Learning objects' metadata (LOM) facilitate adaptive selection of learning objects on the Web as well as the instructional development of Web-based courses. Although there are a number of ongoing LOM standard initiatives a lot of problems with LOMs are reported: reaching from more technical applications (e.g. data integrity deficiencies) to contextualization problems. In this paper, we present an information model for learning objects, capable to solve many of these problems. We use proven object-oriented design patterns for modeling the different metadata elements of learning objects and their relationships.
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