Adaptive Visualization for Focused Personalized Information Retrieval

The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information.Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal.This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently.We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study.We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval.The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information

Adaptación dinámica al usuario en un sistema de enseñanza mediante aprendizaje por refuerzo

Uno de los problemas más importantes de los sistemas de educación a distancia es personalizar la enseñanza a cada estudiante, adaptando su política pedagógica dependiendo de las necesidades de aprendizaje que tengan los estudiantes. Los Sistemas de Educación Adaptativos e Inteligentes en Web (del inglés Web-based Adaptive and Intelligent Educational Systems) (SEAIs en Web) son sistemas de educación basados en Internet donde se aplican técnicas de inteligencia artificial con el objetivo de adaptar el contenido del sistema a los estudiantes. Definir políticas pedagógicas efectivas en estos sistemas es uno de los principales problemas de los SEAIs en Web, decidiendo qué, cómo y cuándo mostrar el contenido del curso a los estudiantes. En el trabajo realizado en la presente tesis doctoral se propone definir el problema de soporte adaptativo a la navegación a través del contenido del sistema y de presentación de dicho contenido como un problema de Aprendizaje por Refuerzo. Al aplicar el modelo de aprendizaje por refuerzo en el módulo pedagógico del sistema, éste será capaz de aprender automáticamente la mejor política pedagógica para cada estudiante individualmente, basados únicamente en la experiencia adquirida con otros estudiantes de características de aprendizajes similares, como hace el tutor en las aulas.___________________________________________________ Last years, distance educational systems have been improved opening new perspectives on different ways to teach. The use of Internet as a tool for the educational systems helps the students and avoid the physical barriers in the access to the classrooms and the incompatibilities due to the different students system platforms. One of the most important issues in distance educational systems is to personalize the teaching to each student, adapting its pedagogical policy according to the pedagogical student needs. The application of artificial intelligence techniques to educational systems allows the systems to adapt in an intelligent way to the students, representing implicitly or explicitly the pedagogical policies, abilities and expert knowledge. The Web-based Adaptive and Intelligent Educational Systems (Web-based AIES) use artificial intelligence techniques in order to adapt the content to the students according to their pedagogical needs. One of the most important issues in these systems is to define effective pedagogical strategies for tutoring students according to their needs. The pedagogical strategies define what, how and when to show the system content to the students. In this PhD. Thesis we propose to use a pedagogical knowledge representation based on the Reinforcement Learning (RL) model. Using this model, the system is able to automatically provide adaptive navigation support and presentation support to the students (choosing the best presentation format for the content). The system learns which is the best pedagogical way to teach each student individually based only on acquired experience with other students with similar learning characteristics, like a human tutor does. In this dissertation we show how the definition of the pedagogical policies in the AIESs can be considered as a Reinforcement Learning problem from a theoretical point of view. Next, we show how the system is able to learn to teach from a theoretical point of view, using simulated students. Finally, we show how the system is able to teach in a practical point of view, interacting with real students of our University through Internet