SEMANTICALLY INTEGRATED E-LEARNING INTEROPERABILITY AGENT

Educational collaboration through e-learning is one of the fields that have been worked on since the emergence of e-learning in educational system. The e-learning standards (e.g. learning object metadata standard) and e-learning system architectures or frameworks, which support interoperation of correlated e-learning systems, are the proposed technologies to support the collaboration. However, these technologies have not been successful in creating boundless educational collaboration through e-learning. In particular, these technologies offer solutions with their own requirements or limitations and endeavor challenging efforts in applying the technologies into their elearning system. Thus, the simpler the technology enhances possibility in forging the collaboration. This thesis explores a suite of techniques for creating an interoperability tool model in e-learning domain that can be applied on diverse e-learning platforms. The proposed model is called the e-learning Interoperability Agent or eiA. The scope of eiA focuses on two aspects of e-learning: Learning Objects (LOs) and the users of elearning itself. Learning objects that are accessible over the Web are valuable assets for sharing knowledge in teaching, training, problem solving and decision support. Meanwhile, there is still tacit knowledge that is not documented through LOs but embedded in form of users' expertise and experiences. Therefore, the establishment of educational collaboration can be formed by the users of e-learning with a common interest in a specific problem domain. The eiA is a loosely coupled model designed as an extension of various elearning systems platforms. The eiA utilizes XML (eXtensible Markup Language) technology, which has been accepted as the knowledge representation syntax, to bridge the heterogeneous platforms. At the end, the use of eiA as facilitator to mediate interconununication between e-leaming systems is to engage the creation of semantically Federated e-learning Community (FeC). Eventually, maturity of the FeC is driven by users' willingness to grow the community, by means of increasing the elearning systems that use eiA and adding new functionalities into eiA

e-Science Infrastructure for the Social Sciences

When the term „e-Science“ became popular, it frequently was referred to as “enhanced science” or “electronic science”. More telling is the definition ‘e-Science is about global collaboration in key areas of science and the next generation of infrastructure that will enable it’ (Taylor, 2001). The question arises to what extent can the social sciences profit from recent developments in e- Science infrastructure? While computing, storage and network capacities so far were sufficient to accommodate and access social science data bases, new capacities and technologies support new types of research, e.g. linking and analysing transactional or audio-visual data. Increasingly collaborative working by researchers in distributed networks is efficiently supported and new resources are available for e-learning. Whether these new developments become transformative or just helpful will very much depend on whether their full potential is recognized and creatively integrated into new research designs by theoretically innovative scientists. Progress in e-Science was very much linked to the vision of the Grid as “a software infrastructure that enables flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions and resources’ and virtually unlimited computing capacities (Foster et al. 2000). In the Social Sciences there has been considerable progress in using modern IT- technologies for multilingual access to virtual distributed research databases across Europe and beyond (e.g. NESSTAR, CESSDA – Portal), data portals for access to statistical offices and for linking access to data, literature, project, expert and other data bases (e.g. Digital Libraries, VASCODA/SOWIPORT). Whether future developments will need GRID enabling of social science databases or can be further developed using WEB 2.0 support is currently an open question. The challenges here are seamless integration and interoperability of data bases, a requirement that is also stipulated by internationalisation and trans-disciplinary research. This goes along with the need for standards and harmonisation of data and metadata. Progress powered by e- infrastructure is, among others, dependent on regulatory frameworks and human capital well trained in both, data science and research methods. It is also dependent on sufficient critical mass of the institutional infrastructure to efficiently support a dynamic research community that wants to “take the lead without catching up”.