Slavomir Stankov

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.

Intelligent Tutoring Systems in E-Learning Environments: Design, Implementation and Evaluation

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Meaningful learning in the tutoring system for programming

Tutoring systems for programming helps students to understand features of target programming language, and develops their general problem solving skill. Our system guides novices in learning syntax and semantics of programming language, problem decomposition, program design and testing. The notional machine defined by programming language and its verbal description of instruction actions helps students to understand semantics of instructions. Advancement through the course material controlled by computer teacher supports connection of new concepts to the present studentpsilas knowledge.

Designing intelligent tutors to adapt individual interaction

Present-day efforts in designing technologies to serve and adapt to human needs rather than forcing humans to adapt, embrace intelligent user interfaces as one of ambient intelligence key technologies. This paper elaborates on the design of an adaptive individual interaction in a type of computer-based educational system whose operation is supported by intelligent methods, an emulation of human teacher in the process of learning and teaching. In order to design interaction simple and effortless as well as to adjust learning process and teaching material to individual student, a mechanism for monitoring students interaction and generating related adaptive interface based on student model is developed. Furthermore, a classification of adaptive hypermedia systems with regard to employed adaptation technology is offered.

Application of semantic web and personal agents in distance education system

Personal agents are proactive and adaptable intelligent agents which meet the demands of individuals. The use of personal agents will become fully prominent when the intelligent agents are provided with mechanisms for understanding the contents of document they access. Making it possible for the intelligent agents to understand semantics of the documents published on the Web is the central task in the development of the Semantic Web. Possibility of application of Semantic Web and the personal agents through distance education systems is elaborated in this paper. We also present adaptation of our distance education system DTEx-Sys (Distributed Tutor Expert System) for the use in environment of Semantic Web and personal agents.

Intelligent tutoring interoperability for the new web

Interoperability of systems based on knowledge is a very important element for reducing their development cost and enabling an easy-to-perform service enrichment. Intelligent tutoring systems (ITSs) may be described as distant learning systems, which base their work on the simulation of the “real” teacher in the learning and teaching process. ITSs base their interoperability on the interchange of domain knowledge, knowledge about learning and teaching process and knowledge about students. This paper describes DiSNeT, a distance learning system we designed based on the intelligent tutoring paradigm, on knowledge presentation using distributed semantic networks and on using agents in the learning and teaching process. We also present a methodology for ensuring interoperability between DiSNeT and other ITSs.

Educational data mining for grouping students in e-learning system

The popularity and the use of e-learning systems increased through last decades. Students produce a lot of data through their interactions with the system, which is often not exploited. Since there is a vast amount of such data, data mining techniques were used as the appropriate solution. In this paper we show practical experience with specific e-learning system and applied data mining technique for the analysis which served as a tool for grouping students with similar characteristics.

TEx-Sys model in the Semantic Web and Web 2.0 environments

The paper presents an approach in solving problems pertinent to intelligent tutoring in the Semantic Web/Web 2.0 environments within the Tutor-Expert System (TEx-Sys) model, an evolving series of ITSs that our group has been developing for the last 15 years, following all web technology generations being used in this period. We describe the way in which contemplating the system as a multi-agent system functioning over the common information space enables solving the problems, additionally extending intelligent tutoring through the mobile, collaborative and social dimensions in the Semantic Web and Web 2.0 environments.