
Due to the difficulty and thus effort and expenses involved in creating them, personalization strategies in learning environments have to demonstrate a higher return-on-investment (ROI), if they are to be a viable component of the learning setting of the future. One feature that can increase this ROI is the reusability of adaptation strategies in Adaptive Educational Hypermedia Systems. This research looks into various ways of enhancing this reusability. Using multiple modular adaptation strategies (MAS) with a controlling meta-strategy is proposed as a more efficient way of authoring adaptation strategies. This renders the reuse of adaptation strategies faster and easier for course authors. A method for semi-automatically breaking down complex adaptation strategies into smaller modular adaptation strategies is described. Potential problems with using multiple strategies are described and ways to solve them are discussed. Finally, some evaluation points are illustrated, conclusions are drawn and further research areas are identified.
QA76
QA76
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
