Branko Žitko

Dynamic test generation over ontology-based knowledge representation in authoring shell

Intelligent tutoring systems are kind of asynchronous e-learning systems designed to support and improve learning and teaching process in particular domain knowledge. An authoring shells are kind of e-learning systems that feature authoring environments for system users. Domain knowledge in such systems can be represented by using different knowledge representation specifications and presentation of tests mainly depends on the type of domain knowledge. We propose templates for dynamical generation of questions as a test over previously formalized domain knowledge. In our approach we encourage expressiveness of ontology for describing domain knowledge. Tests described in this paper entails declarative knowledge formalized by Web Ontology Language (OWL) and are realized as a dynamic quiz. By pronouncing OWL ontology as domain knowledge formalism we deal with the problem of generating tests and understanding presentation of the tests.

Applying graph sampling methods on student model initialization in intelligent tutoring systems

Purpose – In order to initialize a student model in intelligent tutoring systems, some form of initial knowledge test should be given to a student. Since the authors cannot include all domain knowledge in that initial test, a domain knowledge subset should be selected. The paper aims to discuss this issue. Design/methodology/approach – In order to generate a knowledge sample that represents truly a certain domain knowledge, the authors can use sampling algorithms. In this paper, the authors present five sampling algorithms (Random Walk, Metropolis-Hastings Random Walk, Forest Fire, Snowball and Represent algorithm) and investigate which structural properties of the domain knowledge sample are preserved after sampling process is conducted. Findings – The samples that the authors got using these algorithms are compared and the authors have compared their cumulative node degree distributions, clustering coefficients and the length of the shortest paths in a sampled graph in order to find the best one. Origin...