A case-based system for lesson plan construction

Planning for teaching imposes a significant burden on teachers, as teachers need to prepare different lesson plans for different classes according to various constraints. Statistical evidence shows that lesson planning in the Malaysian context is done in isolation and lesson plan sharing is limited. The purpose of this thesis is to investigate whether a case-based system can reduce the time teachers spend on constructing lesson plans. A case-based system was designed SmartLP. In this system, a case consists of a problem description and solution pair and an attributevalue representation for the case is used. SmartLP is a synthesis type of CBR system which attempts to create a new solution by combining parts of previous solutions in the adaptation. Five activities in the CBR cycle retrieve, reuse, revise, review and retain are created via three types of design: application, architectural and user interface. The inputs are the requirements and constraints of the curriculum and the student facilities available, and the output is the solution, i.e. appropriate elements of a lesson plan. The retrieval module consists of five types of search advanced search, hierarchical, Boolean, basic and browsing. Solving a problem in this system involves obtaining a problem description, measuring the similarity of the current problem to previous problems stored in a database, retrieving one or more similar cases and attempting to reuse the solution of the retrieved cases, possibly after adaptation. Case adaptation for multiple lesson plans helps teachers to customise the retrieved plan to suit their constraints. This is followed by case revision, which allows users to access and revise their constructed lesson plans in the system. Validation mechanisms, through case verification, ensure that the retained cases are of quality. A formative study was conducted to investigate the effects of SmartLP on performance. The study revealed that all the lesson plans constructed with SmartLP assistance took significantly less time than the control lesson plans constructed without SmartLP assistance, although they might have access to computers and other tools. No significant difference in writing quality, measured by a scoring system, was noticed for the control group, who constructed lesson plans on the same tasks without receiving any assistance. The limitations of SmartLP are indicated and the focus of further research is proposed. Keywords: Case-based system, CBR approach, knowledge acquisition, knowledge representation, case representation, evaluation, lesson planning

Uso da FAQ como base de casos em um sistema tutor inteligente: Demetrius Ribeiro Lima ; orientadora, Marta Costa Rosatelli

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Ciência da ComputaçãoEste trabalho apresenta um Sistema Tutor Inteligente integrado a um Ambiente Virtual de Aprendizagem, implementando a tutoria inteligente em um curso a distância. Neste contexto, a tutoria inteligente consiste no auxílio e suporte ao aluno durante um curso virtual de modo a orientá-lo no processo de aprendizado, realizando um trabalho de acompanhamento de forma constante. Considerando que a Educação a Distância pode ser definida como um processo de ensino-aprendizagem em que o professor e o aluno estão separados fisicamente, a utilização de recursos tecnológicos que possibilitem a supervisão contínua e imediata deste processo é de grande relevância. O Sistema Tutor Inteligente desenvolvido utiliza como técnica de Inteligência Artificial, o Raciocínio Baseado em Casos, fazendo da Frequently Asked Questions a base de conhecimento do modelo do domínio. Através dela, os casos são recuperados e apresentados ao aluno. O modelo do estudante adapta características do sistema ao perfil do aluno a cada interação deste com o ambiente. As intervenções do sistema, que são acionadas pelo modelo do tutor, são feitas de acordo com este perfil. This work presents an Intelligent Tutoring System that is integrated into a Learning Virtual Environment, implementing intelligent tutoring in a distance course. In this context, intelligent tutoring consists of assisting and supporting the student during a virtual course, aiming to guide the student in the learning process through a permanent accompaniment. Taking into account that Distance Education can be defined as a process of teaching and learning in which the teacher and the student are physically separated, using technological resources that allow immediate and continuous supervising the student is of great relevance. The Intelligent Tutoring System that was developed uses Case-Based Reasoning as an Artificial Intelligence technique to make the Frequently Asked Questions the knowledge base of the domain model. The student model adapts system characteristics to the student profile at each interaction between him or her and the system. The system interventions, which are initiated by the tutor model, are also generated according to the student profile

Razonamiento basado en casos (RBC) para toma de decisiones en proyectos de implementación de sistemas ERP utilizando jColibri

Propone un Modelo de Razonamiento Basado en Casos (RBC) que permita contar con información oportuna, ágil y confiable para la gestión del conocimiento y la toma de decisiones en proyectos de Implementación de Sistemas ERP. Para realizar el presente trabajo se utilizará la herramienta jColibri la cual es un armazón o Framework orientado a objetos que facilita la construcción de sistemas de razonamiento basado en casos (RBC). La implementación del modelo incrementa la calidad de la información y reduce los tiempos utilizados en solucionar los problemas de toma de decisiones de estos proyectos.Trabajo de suficiencia profesiona

Modelo para la extracción de conocimiento de un experto humano en un sistema basado en conocimientos usando razonamiento basado en casos

Los Sistemas Basados en el Conocimiento (SBC) son una técnica de la inteligencia artificial. Su arquitectura se compone de varios módulos, los más citados en la literatura son: interfaz de usuario, generador de explicaciones, motor de inferencia, base de hechos, y base de conocimiento. En el proceso de desarrollo de un SBC, intervienen varios actores, a saber: usuario, ingeniero de conocimiento y experto humano. El éxito de estos sistemas se fundamenta en gran parte en el módulo de adquisición de conocimiento, equivalente a la extracción del conocimiento del experto, donde interviene el Ingeniero de Conocimiento (IC) y el Experto Humano (EH). El IC debe realizar un proceso de adquisición del conocimiento, que implica varias técnicas, además de comprender y aprender los elementos básicos del problema o dominio a resolver, igualmente debe encontrar una forma de representación que interprete el conocimiento del experto, proceso de difícil comprensión en el caso de que este quiera explicar su conocimiento. Lo anterior conlleva a que en ocasiones no se logre un diseño adecuado, debido a errores en los requisitos iniciales obligando al Ingeniero de Conocimiento a realizar ajustes o cambios del diseño. Para superar esta dificultad, esta tesis de doctorado presenta el desarrollo de un modelo, que sirve como punto de referencia para la abstracción del conocimiento en los seres humanos (quienes tienen un saber, y unas competencias propias de cada uno). El modelo aplica elementos de Razonamiento Basado en Casos (RBC), de tal forma que en la búsqueda de encontrar casos recopilados o referenciados que tengan relación con el dominio del problema, sirvan como insumo en el momento de concretar la etapa de adquisición de conocimiento. El modelo solo se orienta en los elementos de fondo de la primera etapa de esta técnica: El razonamiento y los casos que se pueden presentar en la experiencia de un ser humano competente en un área concreta. El modelo fue configurado de tal forma que permitió crear contextos que validaron la parte de análisis, diseño e implementación de un SBC. Fue validado por la población muestra como exitoso, y esta en gestión para ser utilizado por organizaciones que manifiesta su interés en el proceso de transferencia tecnológica.Abstract: The knowledge-based systems (KBS) are active part of artificial intelligence. The architecture of a KBS is composed by modules, user interface, explanation generator, inference engine, working memory, and knowledge base, just among the most cited. In the development of a KBS interact many actors like the user, knowledge engineer, and human expert. The success of a KBS is mainly based on the knowledge acquisition system which is equivalent to the knowledge extraction from the expert, where both the human expert and the knowledge engineer intervene. The engineer should perform a knowledge acquisition process implying the use of many techniques besides of the comprehension and learning of basic elements from the problem or domain to be solved. Likewise, the engineer must find the way to interpret the knowledge from the expert which is the hardest part in a KBS. In order to overcome this fact, the development of a model is proposed. It will help as a starting point or reference to the knowledge abstraction in human beings (for those people having a special knowledge to be perpetuated). The model will apply elements of Case-Based Reasoning (CBR) so that solutions of related problems can be added to the knowledge acquisition system to improve the expert knowledge. The model was designed that creates contexts that validated the part of analysis, design and implementation of KBS. It was validated by the population shows as successful, and there is a management process to be used by organizations that expressed interest in the technology transfer processDoctorad

Case retrieval in CBR-tutor

CBR-Tutor is an Internet agent-based tutoring system that uses case-based reasoning approach in providing adaptive instruction to its learners. Cases can quickly recognize whether a teaching strategy is relevant to apply in a given situation. It is composed of four types of agents. These are the system agent (SA), case facilitator agent (CFA), case-based information agent (CIA) and case-based tutor agent (CTA). The CTAs interact directly with the learner. It has a set of local cases, which are commonly used. If the local cases are not useful for the new situation, the CTA will request retrieval from the global set of cases. The CIAs are responsible for storing and retrieving cases from the global case libraries. These cases contain situations experienced by the CTAs in the system. This paper presents how cases are retrieved in CBR-Tutor. © 2002 IEEE