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Student projects


Metadata-based Adaptivity in E-Learning

Kind of work:
Diploma Thesis
Dr.-Ing. Heinz- Dietrich Wuttke
Dr.-Ing. Ali Diab
Anna Baldaranova
As a result of the growing number of users of e-learning courses on the internet and therefore the increasing diversity of learners’ groups, there is a trend of personalizing online courses and shape them to be as adaptive as possible.
It is essential for the learning content to be designed in an adaptive way, so that the learning process can be organized as efficiently as possible and the usability of the e-learning system can be carried out at the highest possible level. To achieve this purpose a metadata-based adaptivity method could be used according to the content’s structure. This method requires an identification of the relevant learner’s and content’s data.
This study deals with an analysis of the metadata-based adaptivity in e-learning and the identification and integration of the metadata, necessary for the adaptation process. It is carried out as an extensive research of the available literature on the subject, combined with an in-depth analysis of standards for learners and content. The metadata model LOM by the Institute of Electrical and Electronic Engineers IEEE is used for the de-scription of the content and provides foundation for integrating the learners’ and con-tent’s metadata. As a result the standard is upgraded according to the model. The learner’s and content’s metadata as well as the relations between them which refer to the adaptivity play a key role.
The study results in a detailed classification of metadata, relevant to the adaptivity in e-learning. Furthermore a methodology is created to integrate the metadata’s relations into the adaptivity concept. A dimension cube is designed for this purpose, which allows the building of applications for personalized learning content based on learner’s and con-tent’s metadata.