Student Model Initialization Using Domain Knowledge Ontology Representative Subset

In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is described. The approach defines which concepts from domain knowledge should be included in the initial test so the system can make conclusions about what students truly know about domain knowledge. This representative subset of domain knowledge is defined using non-semantic mathematical approach based on graph theory. The initial test, created over a domain knowledge representative subset, guarantees encompassing all concepts that are relevant to domain knowledge. A two-level case study is conducted on what would be the representative subset of one selected domain knowledge. It compares semantically selected domain knowledge representative subsets (semantical analysis was done by domain area experts) to a non-semantical, mathematically selected domain knowledge representative subset. The results of the case study show that problems of inequality of semantically selected domain knowledge representative subsets are easily overcome using the presented approach.