http://hdl.handle.net/10016/8040 Tue, 21 May 2013 15:11:47 GMT 2013-05-21T15:11:47Z http://hdl.handle.net/10016/16418 Title: APElNTA: Apuesta Por la Enseñanza Inclusiva: uso de nuevas tecnologías dentro y fuera del aula Author(s): Iglesias, Ana; Moreno, Lourdes; Castro, Elena; Martínez, Paloma; Jiménez, Javier; Revuelta, Pablo; Sánchez-Pena, José Manuel; Ruiz-Mezcua, Belén Abstract: El objetivo principal del proyecto APEINTA (acrónimo de Apuesta Por la Enseñanza Inclusiva: uso de Nuevas Tecnologías dentro y fuera del Aula) es apostar por una enseñanza de integración e igualdad, donde el gran protagonista es el estudiante. Para ello se propone el uso de las nuevas tecnologías, tanto informáticas como telemáticas, para evitar las barreras de aprendizaje que actualmente persisten en las aulas. Description: Artículo ganador del Premio FIAPAS de investigación en deficiencias auditivas, Área de educación (2009) Thu, 31 Dec 2009 23:00:00 GMT http://hdl.handle.net/10016/16418 2009-12-31T23:00:00Z http://hdl.handle.net/10016/16227 Title: Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining Author(s): Pablo-Sánchez, César de; Segura-Bedmar, Isabel; Martínez-Fernández, Paloma; Iglesias, Ana Abstract: Named Entity Recognition and Classification (NERC) is an important component of applications like Opinion Tracking, Information Extraction, or Question Answering. When these applications require to work in several languages, NERC becomes a bottleneck because its development requires language-specific tools and resources like lists of names or annotated corpora. This paper presents a lightly supervised system that acquires lists of names and linguistic patterns from large raw text collections in western languages and starting with only a few seeds per class selected by a human expert. Experiments have been carried out with English and Spanish news collections and with the Spanish Wikipedia. Evaluation of NE classification on standard datasets shows that NE lists achieve high precision and reveals that contextual patterns increase recall significantly. Therefore, it would be helpful for applications where annotated NERC data are not available such as those that have to deal with several western languages or information from different domains. Mon, 30 Apr 2012 22:00:00 GMT http://hdl.handle.net/10016/16227 2012-04-30T22:00:00Z http://hdl.handle.net/10016/16247 Title: An experience applying reinforcement learning in a web-based adaptive and intelligent educational system Author(s): Iglesias, Ana; Martínez, Paloma; Fernández, Fernando Abstract: The definition of effective pedagogical strategies for coaching and tutoring students according to their needs is one of the most important issues in Adaptive and Intelligent Educational Systems (AIES). The use of a Reinforcement Learning (RL) model allows the system to learn automatically how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. The application of this artificial intelligence technique, RL, avoids to define the teaching strategies by learning action policies that define what, when and how to teach. In this paper we study the performance of the RL model in a DataBase Design (DBD) AIES, where this performance is measured on number of students required to acquire efficient teaching strategies. Fri, 28 Feb 2003 23:00:00 GMT http://hdl.handle.net/10016/16247 2003-02-28T23:00:00Z http://hdl.handle.net/10016/16242 Title: EDU-EX: a tool for auto-regulated IntelligentTutoring systems development based on models Author(s): Domingo-García, Paloma; García-Crespo, Ángel; Ruiz-Mezcua, Belén; Iglesias, Ana Abstract: In recent years there has been an upsurge in forms of instruction that envisage a permanent and ongoing involvement in education of novel concepts such as planned and personalised instruction and autonomous learning. A large number of problems that arise ineducation today may be solved by introducing new technologies into the educational environment, as they allow the form and content of tutoring systems to be tailored to each individual.The application of Artificial Intelligence techniques is helping open up new prospects in the field of teaching and learning. Using Artificial Intelligence techniques in education has the advantage of making it possible to represent expert reasoning and knowledge skills, and to take advantage of this experience in education.This study has involved the development of a tool to generate auto-regulated intelligent tutoring systems based on models. This form of representation makes it possible to break down, organise and represent information so as to enable the easy creation of functionalintelligent computerised tutoring systems. Information about the subject in question, about inference mechanisms, and of a pedagogical nature (independent of any one strategy) is allseparated. The tool also enables knowledge acquired by a student to be constantly monitored with a view to auto-regulating the course contents. Sat, 31 Aug 2002 22:00:00 GMT http://hdl.handle.net/10016/16242 2002-08-31T22:00:00Z