RESEARCH AND MODELING OF TECHNOLOGY-ENHANCED LEARNING PROCESSES FOCUSED ON KNOWLEDGE MANAGEMENT

This article (1) reviews extant learning design process and knowledge management and situates the research problem concerning the modelling approach to study knowledge-intensive learning processes, (2) identifies the SECI knowledge-creation theory of Nonaka governing the design and development of technology-enhanced learning processes complied with the concept of IMS Learning Design(LD) specification, and (3) integrates various social computing applications and technological infrastructure to support different parts of a specific knowledge-creating learning process. The purpose of our study is to explore how the process of tacit knowledge externalisation in an online environment can be enhanced. Thus we need to understand the importance of knowledge conversion process through the use of online networks and services. When designing online learning, it's more important to drive it from the learning challenge rather than the technology perspective. We need to design for learner activity rather than delivery of content. IMS LD allows scenarios to be presented to learners online. It can describe a wide variety of pedagogical models, or approaches to learning, including group work and collaborative learning. The high potential for synergies between knowledge management (KM) and e-learning seems obvious given the many interrelations and dependencies of these two fields. There is an increasing need for systems that seamlessly support knowledge-intensive learning processes to improve knowledge work productivity. However, the relationship is not yet fully understood and harnessed. Current KM technologies focus on knowledge acquisition, storage, retrieval, and maintenance. Regarding the deployment process, learning is considered to be a fundamental part of KM because learners must internalize (learn) shared knowledge before they can use it to perform specific tasks. E-learning systems might also benefit from KM technologies. During the last years, so-called social computing applications, seem to have a positive impact in terms of, knowledge creating and sharing, and community building. First questions arise, to what degree these systems support certain learning processes. In the area of KM, a large part of knowledge is not explicit but tacit. Tacit knowledge is characterised by the fact that it is personal, context specific and therefore hard to formalise and communicate. Explicit, on the other hand, is the knowledge that is transmittable through any systematic language. Human beings acquire knowledge by actively creating and organising their own experiences. Thus, explicit knowledge represents only the tip of the iceberg of the entire body of knowledge. Effective KM requires a continuous knowledge conversion process. Nonaka presents a social process between individuals and not confined within an individual. Given the tacit/explicit conversion process, it is possible to assert that knowledge is social as produced and shared among a network of human and non-human actors within the organisation, for example learning process supported by PCM2.0, ILIAS and Web2.0 tools. With the development of information technologies more knowledge sharing takes place online rather than face-to-face. In an online environment, sharing one’s own experience is the most effective way for people to share their tacit knowledge with others. The implementation of effective online environments facilitates online externalisation of tacit knowledge.