Adaptive tutorial dialogues have been successfully employed by ITSs to facilitate deep learning of conceptual domain knowledge. But none of the approaches used for generating dialogues have been used across instructional domains and tasks. The objective of this project was twofold: (i) to propose a general model that provides adaptive dialogue support in both well- and ill-defined instructional tasks (ii) to explore whether adaptive tutorial dialogues are better than non-adaptive dialogues in acquiring domain knowledge. Our model provides adaptive dialogue support by identifying the concepts that the student has most difficulty with, and then selecting the tutorial dialogues corresponding to those concepts. The dialogues are customised based on the student’s knowledge and explanation skills, in terms of the length and the exact content of the dialogue. The model consists of three parts: an error hierarchy, tutorial dialogues and rules for adapting them. We incorporated our model into EER-Tutor, a constraint-based tutor that teaches database design. The effectiveness of adaptive dialogues compared to non-adaptive dialogues in learning this ill-defined task was evaluated in an authentic classroom environment. The results revealed that the acquisition of the domain knowledge (represented as constraints) of the experimental group who received adaptive dialogues was significantly higher than their peers in the control group with non-adaptive dialogues. We also incorporated our model into NORMIT, a constraint-based tutor that teaches data normalization. We repeated the experiment using NORMIT in a real-world class room environment with a much smaller group of students (18 in NORMIT study vs 65 in EER-Tutor study) but did not find significant differences. We also investigated whether our model could support dialogues in logical database design and fraction addition using paper-based methods. Our evaluation studies and investigations on paper indicated that our model can provide adaptive support for both ill-and well-defined tasks associated with a well-defined domain theory. The results also indicated that adaptive dialogues are more effective than non-adaptive dialogues in teaching the ill-defined task of database design.

<p>Predmet istraživanja disertacije obuhvata&nbsp; realizaciju&nbsp; op&scaron;teg&nbsp; modela tutorskog<br />sistema za elektronsko učenje iz različitih domena&nbsp; primenom&nbsp; tehnologija semantičkog<br />veba i primena tog modela za&nbsp; izgradnju tutorskog sistema za učenje programskog jezika&nbsp;Java sa elementima personalizacije.<br />Cilj disertacije je implementacija&nbsp; i predstavljanje&nbsp; svih&nbsp; elemenata&nbsp; tutorskog sistema za<br />učenje programskog jezika Java&nbsp; pomodu tehnologija semantičkog veba. Ovaj proces<br />obuhvata kreiranje&nbsp; osnovnih&nbsp; gradivnih&nbsp; ontologija&nbsp; kao i&nbsp; pravila za izvođenje konkretnih<br />akcija kojim se postiže personalizacija nastavnog materijala.</p> / <p>The subject of the dissertation includes the implementation of a conceptual&nbsp;model of tutoring system for e-learning in different domains using semantic web&nbsp;technologies and application of that model in a design of a tutoring system for&nbsp;personalised learning of Java programming language.</p><p>The goal of the dissertation is the implementation and presentation of all elements of the tutoring system for learning the Java programming language using semantic web&nbsp;technologies. This process includes the creation of the fundamental building blocks of&nbsp;ontologies and rules for carrying out the actions for adaptation of teaching materials.</p>