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For many years, ITS researchers have strived to provide better instruction. They have various kinds of student models and expert models, as well as models of student-tutor interactions. However, little research have been conducted on in-structional planning, which attempts to make a sequence of instructions optimal for a student. Obviously, the bet...
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... for each state will never change throughout the tutoring session. For example, a value of the state where a student knows a particular concept should not be changed no matter when he learns the concept. Thus, once an optimal policy for each state is settled, the ITS can determine the action to be taken by simply looking up a table of the policy. Fig. 2 shows basic cycle of the instructional planner. The planner reads an optimal action to be taken off the policy table. The executer applies the selected action and passes the evaluator a set of expected responses specified as the resulted states of the action. The evaluator then compares the student's response with the expected ...
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... La planificación instruccional (instructional Planning, IP) constituye una de las tareas más importantes en los sistemas educativos para lograr la adaptación de la instrucción al aprendiz. Es el componente encargado de determinar la secuencia de las acciones (Plan) de tutorización de manera consistente, coherente y continua las cuales maximizan las actividades de aprendizaje de cada alumno para alcanzar los Objetivos Instruccionales durante una sesión de aprendizaje (Matsuda & VanLehn, 2000). ...
En este artículo se presenta un ambiente inteligente distribuido de enseñanza y de aprendizaje, usando las técnicas de la inteligencia artificial denominadas Razonamiento Basado en Casos (CBR) y Planificación Instruccional (IP). El ambiente fue modelado utilizando el abordaje de los sistemas multi-agente, el cual le suministra al ambiente las características de adaptabilidad, autonomía y flexibilidad. El modelo pedagógico del ambiente se fundamenta en los paradigmas del conductismo, cognitivismo e histórico-social.
... The IP (Instructional Planning) constitutes one of the most important tasks in the educative systems for obtaining the adaptation of the instruction to the apprentice; it is the component in charge of determining the sequence of the actions (Plan) of tutoring in a consistent, coherent and continuous way, which maximizes the learning activities of each apprentice to achieve the IOs during a learning session [22]. The first systems of the years 60s had a conditional instructional planning prefixed by a teacher. ...
This paper presents a description of the environments of individualized learning (Based on the Intelligent Tutoring Systems, ITS), the Computer Supported Collaborative Learning (CSCL), Multi-Agent Systems (MAS) and the artificial intelligence techniques called: Instruc- tional Planning (IP) and Case-Based Reasoning (CBR). Finally ALLEGRO is presented, a MAS environment of support to the teaching/learning process that includes all previous artificial in- telligence elements.
... La Planificación Instruccional (IP) constituye una de las tareas más importantes en los sistemas educativos para lograr la adaptación de la instrucción al aprendiz. Es el componente encargado de determinar la secuencia de las acciones de tutorización [12] de manera consistente, coherente y continua [13], las cuales maximizan las actividades de aprendizaje de cada alumno para alcanzar los objetivos instruccionales durante una sesión de aprendizaje [14]. La evolución de las técnicas de IPs ha sido paralela a los avances de las técnicas de resolución de problemas en la AI [15]. ...
RESUMEN En este artículo se presenta un mecanismo de planificación instruccional (IP) utilizando la técnica de aprendizaje automático de la inteligencia artificial (AI) denominada razonamiento basado en casos (CBR). El mecanismo fue implementado en el Ambiente Multi-Agente de Enseñanza/Aprendizaje ALLEGRO, el cual fundamente su paradigma instruccional en tres modelos pedagógicos: conductismo, cognitivismo (cognición distribuida y aprendizaje basado en problemas) y la teoría histórico-social. La validación fue realizada a través de diversos casos de estudio en el dominio de gráfica digital para cursos de postgrado en arquitectura.
... All rights reserved. Matsuda and VanLehn 2000;Murray and VanLehn 2000;Zapata-Rivera and Greer 2001). Recently, Reddy challenged AI researchers with three open problems (Reddy 2003). ...
It is a vital and challenging issue in AI community to get the "Right Information" to the "Right People" in the "Right Language" in the "Right Timeframe" in the "Right Level of Granularity" . More and more researchers recognized that a well-designed instructional plan in intelligent tutoring systems will open up new possibilities of genuinely intelligent knowledge delivery for future education society. This paper proposes a novel three-layer student-state model and discusses the core elements in the architecture of a practical ITS model based on the theory of instructional automata. The main advantages of instruction automata theory lie in that it can not only generate, regulate, update and implement instructional plans for individualized learner in the efficient and effective way, but also provide a uniform and extensible domain-independent environment for ITS designers and engineers.
... La Planificación Instruccional (IP) constituye una de las tareas más importantes en los sistemas educativos para lograr la adaptación de la instrucción al aprendiz. Es el componente encargado de determinar la secuencia de las acciones de tutorización (Woo et al 2005) de manera consistente, coherente y continua (Wasson 1990), las cuales maximizan las actividades de aprendizaje de cada alumno para alcanzar los objetivos instruccionales durante una sesión de aprendizaje (Matsuda & VanLehn 2000). ...
In this paper, the application of plausible reasoning in different domains has been investigated. The theory of plausible reasoning has been introduced on early 1990s. However after that there has not been a significant work to show its applicability to real world problems. After several years of experimentation in different fields such as Information Retrieval, Information Filtering and Intelligent Tutoring systems, we feel confident that this theory could be applied to real world problems instead of or in conjunction with other theories such as Fuzzy logic, Dempster-Shaffer theory of evidence etc. Here we describe several of our implementations and experiments with this theory.
This paper is aimed at describing collaborative activities, the awareness process and blackboard architecture used within the ALLEGRO system, a multi-agent learning and teaching environment which has included artificial intelligence mechanisms (instructional planning (IP), cased-based reasoning (CBR), multi-agent systems (MAS), intelligent tutoring systems (ITS) and computer supported collaborative learning (CSCL)). The proposed model of awareness promotes communication, collaboration and coordination in CSCL ALLEGRO. MAS-CommonKADS methodology was applied for building the Multi-Agent system. ALLEGRO based its instructional approach on the following three pedagogical paradigms: conductive behaviour, cognitive mechanisms (distributed cognition and problem-based learning) and social-historical theory. The system was validated via several case studies regarding the graphical digital domain for postgraduate architectural studies. The proposed awareness model lets students develop important skills such as conscious behaviour, communication, collaboration and coordination in the CSCL component of the ALLEGRO system. This model facilitates students acquiring a perception of what others are doing inside the CSCL, thereby using such knowledge for interacting with the environment and developing leading to expressing and exchanging ideas and opinions with other students, transforming the class into an open virtual forum for reflection, proactive interaction and a critical space for exchanging ideas. The proposed
