An adaptive feedback approach for e-learning systems

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.