Abstract:
The adaptive e-learning systems are a hot topic of
educational research. The approach presented is a
knowledge-based. There are several types of adaptation of an
e-learning system to the learner: content adaptation, interface
personalization, etc. This paper dials with a model for adaptation
of the learner assessment and the content of one learning system.
The model is based on Computer Adaptive Test Theory (CAT)
and organization of the learning domains. The learning objects
(LO) and the test item ontology play a central role as resource
structuring. It supports flexible adaptive strategies for assessment
and navigation through the content. Learner knowledge is
assessed by CAT and then the system returns the learner to the
right leaning material corresponding to the knowledge shown.
The congruence between CAT item bank and the LO pool is based
on intelligent agents. It supports adaptive feedback to the students
depending on the learner evaluation.
