Using decision models for the adaptive generation of learning spaces

This paper presents an approach that uses a decision model for resolving variations in a so-called learning space, which aim is to enhance the reuse of explicitly documented experiences by providing context-aware learning content. Decision models promise a better possibility to separate the variabilities in e-learning content, and address the problem of closed corpus of adaptive hypermedia systems. Adaptation is not coupled to a fixed set of learning resources, but to types of learning space concepts. The system adapts and personalizes the learning space to the learner's situation. A controlled experiment provides first statistically significant results, which show an experience package reuse improvement regarding knowledge acquisition and application efficiency. Further, it provides a baseline for future evaluations of different adaptation methods and techniques.