The Enhancement of Reusability of Course Content and Scenarios in Unified e-Learning Environment for Schools

With the expansion of e-learning, the understanding and evaluation of already created e-learning environments is becoming an extremely important issue. One way to dealing with the problem is analysis of case studies, i.e. already created environments, from the reuse perspective. The paper presents a general framework and model to assess UNITE, the unified e-learning environment for schools, from the reuse perspective. UNITE is the e-learning environment of the ongoing EU project (FP6 IST-26964, 2006-2008, http://www.unite-ist.org/). UNITE assets are described using feature diagrams (FDs) telling us about the internal structure of UNITE; representing relationships among the compound and atomic features, thus enhancing better transparency of UNITE and in this way empowering reuse. The factors of UNITE influential to reuse with some concrete results are also presented. We provide analysis aiming to extract from the model the relevant information of two kinds: (1) which is influential to reuse in a positive sense, i.e., enhancing reuse (e.g., application of meta-design methodology for the scenarios description, classification of subjects in metadata, use of content management tools (e.g., Course editor, Metadata editor), multi-linguistic approach, international and local collaboration between teachers and students in e-learning scenario implementation and delivery, and methodological support, etc.) and (2) which is hindering reuse (e. g., age of the students, differences in national syllabus and national educational programmes, language, cultural and communication problems). Despite of some limitations of FDs, we found this notation useful because it allows the explicit representation of various aspects of the complex system (i.e., UNITE) focusing on variability of features and possible relationships and constraints. We focus on the aspects such as evaluation of the UNITE platform including tools, scenarios and content variability.