Ani Grubišić

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.

Ontology based approach to Bayesian student model design

Probabilistic student model based on Bayesian network enables making conclusions about the state of student’s knowledge and further learning and teaching process depends on these conclusions. To implement the Bayesian network into a student model, it is necessary to determine “a priori” probability of the root nodes, as well as, the conditional probabilities of all other nodes. In our approach, we enable non-empirical mathematical determination of conditional probabilities, while “a priory” probabilities are empirically determined based on the knowledge test results. The concepts that are believed to have been learned or not learned represent the evidence. Based on the evidence, it is concluded which concepts need to be re-learned, and which not. The study described in this paper has examined 15 ontologically based Bayesian student models. In each model, special attention has been devoted to defining “a priori” probabilities, conditional probabilities and the way the evidences are set in order to test the successfulness of student knowledge prediction. Finally, the obtained results are analyzed and the guidelines for ontology based Bayesian student model design are presented.

Controlled experiment replication in evaluation of e-learning system's educational influence

We believe that every effectiveness evaluation should be replicated at least in order to verify the original results and to indicate evaluated e-learning systems advantages or disadvantages. This paper presents the methodology for conducting controlled experiment replication, as well as, results of a controlled experiment and an internal replication that investigated the effectiveness of intelligent authoring shell eXtended Tutor-Expert System (xTEx-Sys). The initial and the replicated experiment were based on our approach that combines classical two-group experimental design and with factoral design. A trait that distinguishes this approach from others is the existence of arbitrary number of checkpoint-tests to determine the effectiveness in intermediate states. We call it a pre-and-post test control group experimental design with checkpoint-tests. The gained results revealed small or even negative effect sizes, which could be explained by the fact that the xTEx-Syss domain knowledge presentation is rather novel for students and therefore difficult to grasp and apply in earlier phases of the experiment. In order to develop and improve the xTEx-Sys, further experiments must be conducted.

Knowledge Tracking Variables in Intelligent Tutoring Systems.

In this research we propose a comprehensive set of knowledge indicators aimed to enhance learners’ selfreflection and awareness in the learning and testing process. Since examined intelligent tutoring systems do not include additional messaging features, the introduction of common set of knowledge indicators differentiates our approach from the previous studies. In order to investigate the relation between proposed knowledge indicators and learner performance, the correlation and regression analysis were performed for 3 different courses and each examined intelligent tutoring system. The results of correlation and regression analysis, as well as learners’ feedback, guided us in discussion about the introduction of knowledge indicators in dashboard-like visualizations of integrated intelligent tutoring system.

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...