No description available.

No description available.

No description available.

Empirical studies have shown that Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing an ITS is a labour-intensive and time-consuming process. A major share of the development effort is devoted to acquiring the domain knowledge that accounts for the intelligence of the system. The goal of this research is to reduce the knowledge acquisition bottleneck and enable domain experts to build the domain model required for an ITS. In pursuit of this goal an authoring system capable of producing a domain model with the assistance of a domain expert was developed. Unlike previous authoring systems, this system (named CAS) has the ability to acquire knowledge for non-procedural as well as procedural tasks. CAS was developed to generate the knowledge required for constraint-based tutoring systems, reducing the effort as well as the amount of expertise in knowledge engineering and programming required. Constraint-based modelling is a student modelling technique that assists in somewhat easing the knowledge acquisition bottleneck due to the abstract representation. CAS expects the domain expert to provide an ontology of the domain, example problems and their solutions. It uses machine learning techniques to reason with the information provided by the domain expert for generating a domain model. A series of evaluation studies of this research produced promising results. The initial evaluation revealed that the task of composing an ontology of the domain assisted with the manual composition of a domain model. The second study showed that CAS was effective in generating constraints for the three vastly different domains of database modelling, data normalisation and fraction addition. The final study demonstrated that CAS was also effective in generating constraints when assisted by novice ITS authors, producing constraint sets that were over 90% complete.

Across many domains, Intelligent Tutoring Systems (ITSs) are used to facilitate practice, providing a customized learning environment and personal tutoring experience for students to learn at their own pace through effective student modeling and feedback. Most current ITSs are built around cognitive learning theories including Ohlsson's theory on learning from performance errors and Anderson's ACT theories of skill acquisition which focus primarily on providing negative feedback or corrective feedback, facilitating learning by correcting errors. Research into the behavior and methods used by expert tutors suggest that experienced tutors use positive feedback quite extensively and successfully. This research investigates positive feedback; learning by capturing and responding to correct behavior, supported by cognitive learning theories. The research aim is to develop and implement a systematic approach to delivering positive feedback in Intelligent Tutoring Systems, in particular SQL-Tutor, a constraint-based tutor which instructs users in the design of Structured Query Language (SQL) database queries. An evaluation study was conducted at the University of Canterbury involving a control group of students who used the original version of SQL-Tutor giving only negative feedback and an experimental group using the modified version of SQL-Tutor where both negative and positive feedback were given. Results of the study show that students learn quite similarly from one system to another, however those in the experimental group take significantly less time to solve the same number of problems, in fewer attempts compared to those in the control group. Students in the experimental group also learn approximately the same number of concepts as students in the control but in much less time. This indicates that positive feedback results in increased amount of learning over a shorter period of time and improves the effectiveness of learning in ITSs.

This thesis presents an intelligent system for tutoring phonetic transcription in introductory linguistics courses. It compares and contrasts this system with previous intelligent tutoring systems and presents an implementation of the present system. The problems and solutions encountered in implementing the system are described.Among the contributions and innovations are the fact that this system guides the student through several attempts at transcribing a word with increasingly specific feedback, and the fact that the system is organized in such a way that an instructor can add, modify or delete data at any time with no assistance required from a programmer.A significant contribution of this system lies in the fact that although there is only one correct answer for any given item to be transcribed, the possibilities for the student's responses and hence for incorrect answers must be open-ended. The student's answer will be a string that may not have the same length as the correct answer, may contain few or none of the same symbols as the correct answer, and those that it does contain may be in a different order. The student's answer is intended to correspond to the correct answer, but is known not to be an exact match. Arbitrary strings representing the student's answers must thus be matched up with the pattern of the correct answer in such a way that the system can give the student meaningful comments that will aid the student in identifying errors. The usual pattern recognition program is designed to identify instances where a match succeeds. This tutor must identify instances where the match fails as well as how it fails. / Department of Computer Science

This thesis describes an Intelligent Tutoring System developed to be used in distance education at Master&rsquo / s level. The system was designed and implemented to help teachers to generate course material for online tutoring and to help the students to navigate through the course material according to their knowledge level. The system integrates many new technologies and provides individualized learning for students which is one of the most efficient ways for learning. How well the student has learned the course material is tested immediately after each smallest learning unit by end-of-section tests and the knowledge level of the student is derived from the answers given to these tests. This knowledge level is used to build a user model. This thesis describes how this user model is used for navigational support for students while studying on the course material.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaf 210). Also available via the Internet.

Thesis (M.S.)--Rochester Institute of Technology, 1992. / Typescript. Includes bibliographical references (leaves 123-125).

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2008. / Includes bibliographical references.