Adapting E-Learning material allows re-using existing material even in changed usage scenarios. But this adaptation is a complex task. To achieve a perfect result expert knowledge is needed. Often adaptations have to be performed by persons who are not experts in performing these tasks. To enable those persons to achieve a satisfying result they need to be supported. Patterns are one possibility to make expert knowledge on certain tasks available to other persons. In this paper an approach is presented how expert knowledge of performing adaptations of existing E-Learning material can be collected and made available with patterns. This approach can also be used to collect knowledge of other processes, e.g. in companies.
The knowledge is defined as combination and organization of data and information in given context and Knowledge Management (KM) provides capturing, storing and reusing of knowledge objects. In Web 2.0 world the knowledge is represented in form of microcontent object and KM 2.0 proposes creation, sharing and leveraging the microknowledge in a collaborative way. The microknowledge in Web 2.0 network can be controlled through designing the instructional strategies that will provide user learning paths and activities and that will give possibilities for sharing of those same learning activities and microknowledge with others. The present paper is aimed to reflect of the research needs and the new challenges in the mentioned above three areas: KM, Web 2.0 technologies and Learning Design (LD). It is focused on designing of personalized learning using IMS LD elements. The paper analyzes the possibilities of applying Web 2.0 technologies for defining a broaden set of activities and creating the rich environments with microknowledge objects and web services in one successful scenario.
In this paper, a terminological and pragmatic paradigm shift is proposed and undertaken from the field of Personalisation Systems towards the field of Adaptive Systems. A new conceptual framework for both topics is developed in order to enable a deeper insight into the challenges and benefits of merging the fields. The aim of this paper is to define a generic and component-based Personalisation Model (PM), which is derived from an analytical perspective on systems that are pertinent to adaptation. Furthermore, validity and applicability of the PM are demonstrated for the field of adaptive e-learning. Thus, practical experiences within the AdeLE (Adaptive e-Learning with Eye-Tracking) research project are discussed.