Multi-level pedagogical model for the personalization of pedagogical strategies in intelligent tutoring systems

Pedagogic strategies are action plans designed to manage issues related to sequencing and content organization, specifying learning activities, and deciding how to deliver content and activities in teaching processes. In this paper, we present an approach to personalization of pedagogical strategies in Intelligent Tutoring Systems using pedagogical knowledge rules in a Web environment. The adaptation of pedagogical strategies is made based on a multilevel pedagogical model. An Intelligent Tutoring Systems called FUNPRO was developed to validate the multilevel pedagogical model. The results of empirical tests show that the multilevel pedagogical model enables FUNPRO to improve the process of personalization of pedagogical strategies, due to the reduction of inappropriate recommendations

Modelo multi-agente para la planificación instruccional y selección de contenidos en cursos virtuales adaptativos

Con el crecimiento y popularidad del Internet los cursos virtuales están volviéndose más atractivos y útiles, sin embargo, la mayoría de estos no son más que una red de páginas con contenido estático. Dichos cursos virtuales no están desarrollados bajo estructuras que permitan llevar a cabo planificación instruccional y tampoco cuentan con modelos para la selección de contenidos educativos. El objetivo principal de esta tesis de maestría es el desarrollo de un modelo para la planificación instruccional y selección de contenidos en cursos virtuales adaptativos, basado en el paradigma multi-agente, el cual permitirá brindar un apoyo efectivo en los procesos de enseñanza-aprendizaje que se llevan a cabo en estos cursos. Este modelo posibilitará la planificación instrucconal teniendo en cuenta el nivel de conocimientos de los estudiantes y la selección de contenidos educativos teniendo en cuenta los estilos de aprendizaje de los estudiantes. / Abstract. With the growth and popularity of Internet, virtual courses are becoming more useful and attractive; however, most of them are nothing more than a network of pages with static content. These virtual courses are not developed under structures that allow instructional planning and also including models for the selection of educational content. The main objective of this work is to develop a model for instructional planning and selection of content in adaptive virtual courses, based on multi-agent paradigm, which will provide effective support in the teaching-learning processes that take place in these courses. This model will allow instructional planning taking into account the level of knowledge of students, and selection of educational content taking into account the learning styles of students.Maestrí

Modelo de planificación instruccional en sistemas tutoriales inteligentes – (junio 2009)

Con el crecimiento y popularidad del Internet los sistemas de educación basados en la Web se están volviendo más y más atractivos, sin embargo, la mayoría de éstos no son más que una red de páginas con contenido estático. Para mejorar estas aplicaciones se debería ofrecer más adaptabilidad, permitiendo de esta manera adecuar los contenidos educativos a las características de los usuarios. Por lo tanto, el objetivo de este artículo es presentar un modelo de planificación Instruccional que pueda ser aplicado en los Sistemas Tutoriales Inteligentes. Dicho modelo se fundamenta en el nivel de conocimientos de los estudiantes, en la teoría de planificación de la Inteligencia Artificial (IA) y en la estructura de cursos aplicada en el Sistema Tutorial Inteligente CIA (Cursos Inteligentes Adaptativos)

Reference model for adaptive and intelligent educational systems supported by learning objects

Abstract: Computer Aided Learning, known more widely with the generic name of e-learning, has become a powerful tool with lots of potentialities within educational field. Even though, one of the main critics that it receives is that in most cases the implemented courses follows a “one size fits all” approach, which means that all students receive the same content in the same way being unaware of their particular needs. This problem is not due only to the absence of direct interaction between student and tutor, but also because of the lack of an appropriate instructional design. There are several approaches which deal with this issue and look for adapt the teaching process to students. One could say that in the top of those approaches the Adaptive and Intelligent Educational Systems are situated, which merges the functionalities of two approaches: the Adaptive Educational Hypermedia Systems and the Intelligent Tutoring Systems. Nevertheless, after an extensive literature review, a major inconvenience is still found for this kind of systems and particularly for their reference models: or they are too simple, including just a few functionalities; or they are too complex, which difficult their design and implementation. Considering this panorama, the main objective of this dissertation thesis was the definition of a reference model trying to reach such an elusive equilibrium, in such a way that allows the design of courses which adapt themselves in an intelligent and effective way to the progress and characteristics of each student but without being too complex. Another important feature is that this model integrates Learning Objects, promoting this way flexibility and reusability. In order to achieve this general objective, three sub-models were considered: a domain model, a student model and a tutor model. The first one serves to structure the knowledge domain and was defined using the notion of learning goal and a flexible multilevel schema with optional prerequisite operations. The second one aids to characterize students and considered personal, knowledge and psycho-cognitive information. The third one may be considered as the hearth of the system and defines the adopted adaptive functionalities: sequencing and navigation, content presentation, assessment, and collaborative support. With the aim of clarify the three sub-models, as well as all their components and relationships, an instantiation example was also presented. Such an instantiation was called Doctus, an authoring tool for adaptive courses. Doctus was not only helpful to exemplify the setup of the referece model as a whole, but also to refine sub-models and several procedures envolved. As final part of the dissertation, the implementation and preliminary validation of Doctus was performed. This was done with 51 subjects, teachers from different formation levels. The obtained results in this stage were outstanding, all the adaptive functionalities were well evaluated and all of those polled felt enthusiastic about counting with a tool for helping them in their teaching practices considering students as particular individuals.Doctorad