| Standards for describing Learning Objects (LO) are becoming increasingly important as exemplified by the work of several organizations most notably the IEEE and the IMS Global Learning Consortium. These efforts continue to advance the goals of interoperability for learning objects, but so far do not address the need for accessibility to their internal composition. Under the present standards the learning object remains an opaque entity from which valuable information cannot be easily gleaned or harvested, nor introduced or adapted. Neither the need for introducing adaptive features to the learning objects, nor the need for learner modeling is well addressed.; This dissertation proposes a hierarchical approach to the design of learning objects (HALO), which is based on open standards, mostly XML markup languages and related tools, and on generic support agents. It defines an architecture for collaborative authoring, for extraction of the results of learner-to-LO interaction, for learning object adaptation, and learning object delivery through generic agents. We specify as inner-metadata the collection of layered metadata mechanisms that are used to describe these hierarchical, adaptive learning objects (HALOs), their adaptive features, and the learner interaction tracing. Learning objects designed according to this architecture still retain aspects that an author would like to reserve for future enhancement and growth, yet they provide an open architecture that supports collaborative LO authoring, adaptation and learner interaction tracing. |
