Thesis (Ph.D.)--Drexel University, 2009. / Includes abstract and vita. Includes bibliographical references (leaves 105-114).

As the amount of accessible and shareable knowledge increases, it is figured out that learning platforms offering the same context and learning path to all users can not meet the demands of learners. This issue brings out the necessity of designing and developing adaptive hypermedia systems. This study describes an agent-based adaptive learning framework whose goal is to implement effective tutoring system with the help of Artificial Intelligence (AI) techniques and cognitive didactic methods into Adaptive Educational Hypermedia Systems (AEHS) in the domain of Unified Modeling Language (UML). There are three main goals of this study. First goal is to explore how supportive agents affect student&rsquo / s learning achievement in distance learning. Second goal is to examine the interaction between supportive agents and learners with the help of experiments in Human Computer Interaction laboratories and system analysis. The effects of the methodology that agents give misleading hints which are common mistakes of other learners are also investigated. Last goal is to deliver effective feedback to students both from IAs and tutors. In order to assess that UML-ALF has accomplished its objectives, we followed an experimental procedure. Experimental groups have taken the advantage of adaptive and intelligent techniques of the UML-ALF and control groups have used the traditional learning techniques. The results show that there is a positive correlation between variables practice score and number of agent suggestion which means, as the participants benefit from supportive agents, they get higher scores.

Distance Education expands the scope of the inside classroom teaching model since anybody, independent of where he lives or how much available time he has, has the opportunity to initiate or complement his studies. Considering access facility, availability and interaction resources provided, the Internet is a midia sufficiently used for Distance Education. This work has as objective the design and implementation of a framework, FA_PorT,for Portfolio-Tutoring systems coupled a Intelligent Tutoring Systems (STI) and using agents and present two applications Portfolio-Tutoring, created from the framework The FA_PorT mainly aiming the design of a model computer-based interactive learning environment for Portfolio-Tutoring systems. Each system Portfolio-Tutoring will be able to be used in the context of environment for EAD in the web. The framework proposed, aims to reuse of internal architecture (or skeleton) of a portfolio-tutoring system based on ITS and Agents, doing with that development time of a new portfolio-tutoring system will be reduced. / A Educação a Distância (EAD) amplia o alcance da modalidade de ensino presencial, já que fornece aos indivíduos, independentemente do local onde moram ou tempo disponível, a oportunidade de iniciar ou complementar seus estudos. Em virtude da facilidade de acesso, disponibilidade e recursos de interação existentes, a Internet é uma mídia bastante utilizada na EAD. O objetivo deste trabalho é projetar e implementar um framework, FA_PorT, para sistemas Portfólio-Tutor acoplado a um sistema Tutor Inteligente (STI) utilizando Agentes e apresentar duas aplicações Portfólio-Tutor, criadas a partir do framework. O FA_PorT visa propor um modelo de ambiente interativo de ensino e aprendizagem baseado numa arquitetura para sistemas Portfólio-Tutor. Cada sistema Portfólio-Tutor construído a partir do framework pode ser utilizado no contexto de ambientes de EAD na web. O framework proposto objetiva o reaproveitamento da estrutura interna (núcleo comum) de um sistema Portfólio-Tutor baseado em STI e Agentes, fazendo com que o tempo de desenvolvimento de um novo sistema Portfólio-Tutor seja reduzido