The economy is not keeping pace with the increasing speed of technological evolution. The inadequacy of the current system of education is a possible reason for this. Evolution forces us to produce experts for tasks and businesses which do not yet exist, to teach them technologies which have not yet been devised. The best way to produce experts is to accentuate the learner's best abilities and skills, assess the learner's potential and develop it further. We badly need revolutionary methods to facilitate intelligent personalization of study processes and approaches to make innovative education content more attractive and motivational for the learner. Advanced information management services and platforms play a valuable role in education process development, enabling new generations of students and education-related content providers to create, share, search, combine and deliver reliable and competent information. Earlier learner involvement in study content co-creation or personalization processes might dramatically increase student motivation and speed up the study process. Like any other products or services, e-Learning services need marketing to attract customers and make them a valuable source. To achieve a vision of ubiquitous knowledge, the next generation of innovative education environments will apply the achievements of the Open Data initiative and move towards learner-driven society-oriented systems. Therefore, this paper touches on different aspects of co-creative innovative education environment and correspondent e-Learning marketing strategies.

Expectations regarding the new generation of Web depend on the success of Semantic Web technology. Resource Description Framework (RDF) is a basis for explicit and machine-readable representation of semantics. However RDF is not suitable for describing dynamic and context-sensitive resources (eg. processes). We present the Context Description Framework (CDF) as an extension of the RDF by adding a 'TrueInContext' component to the basic RDF triple ('subject-predicate-object'), and consider contextual value as a container of RDF statements. We also add a probabilistic component, which allows multilevel contextual dependence descriptions as well as presumes possibility for Bayesian reasoning with the RDF model.

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Abstract:Agent-oriented approach has proven to be very efficient in engineering complex distributed software environments with dynamically changing conditions. The efficiency of underlying modelling framework for this domain is undoubtedly of a crucial importance. Currently, a model-driven architecture has been the most popular and developed for purposes of modelling different aspects of multi-agent systems, including behaviour of individual agents. UML is utilized as a basis for this modelling approach and variety of existing UML-based modelling tools after slight extension are reused. This paper proposes an ontology-driven approach to modelling agent behaviour as an emerging paradigm that originates from the Semantic Web wave. The proposed approach aims at modelling a proactive behaviour of (web-)resources through their representatives: software agents. In general, the presented research puts efforts into investigation of beneficial features of ontology-based agent modelling in comparison with conventional model-driven approaches.

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