Adapting SCORM Compliant LOs in a Knowledge Engineering Scenario

Abstract

The success of communities generally implies both growth of the participants (and thus of their contributions), and an increasing array of services offered. For this reason, it is often difficult for new users to fully understand the potential of the community, while for expert users it becomes difficult both to manage the resources available in an effective and efficacious way, and to keep track of the expansion of the community. In order to overcome this problem it is necessary to provide the user with some teaching about the changes occurring in the community. The present article focuses on the problem of personalising such teaching, tailoring it to the user's cognitive and learning styles in order to supply the didactic material in the form best suited to the user, and thus facilitate learning of the knowledge sharing environment. Tailoring the teaching strategy is an innovative issue in the context of knowledge sharing communities.∀ LO Format (LO, X) and (is(X, mpeg3) or is(X, Waw)) => ObjectType (LO, Audio) ∀ LO LearningResourceType(LO, X) and (is (X, diagram) or is (X, figure) or is (X, graph)) => visualResource(LO)Rules for the visual student's resources ∀ LO LearningResourceType(LO, X) and (is (X, narrative text) or is (X, lecture) or is (X, table)) => verbalResource (LO) Rules for the verbal student's resources ∀ LO LearningResourceType(LO, X) and (is (X, experiment) => Activity (LO, Pratical) Rules for the practical student's resources ∀ LO LearningResourceType(LO, X) and is (X, simulation) => reflectiveResource(LO) Rules for the reflective student's resources ∀ LO Structure (LO, X) and is(X, Atomic) => AggregationLevel (LO, 1) Structure is an element in the General category; if it has the value "Atomic" then it has a low level of aggregation ∀ LO Structure (LO, X) and is(X, Collection) => AggregationLevel (LO, 3)If a resource is a collection of many simple elements it can be inferred that it has a medium level of aggregation