E‐learning agents

Gregg, Dawn G.

doi:10.1108/09696470710749245

Cited by 39 publications

(17 citation statements)

References 43 publications

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“…Por otra parte, Gregg (2007) propone un ambiente que puede usarse para proporcionar aprendizaje personalizado, usando un conjunto de agentes interactivos que personalizan la instrucción basados en el conocimiento previo individual así como las necesidades cognitivas y de aprendizaje del estudiante, y en esta misma tendencia, Sun, Joy y Griffiths (2007) desarrollan un sistema de educación multiagente que incorpora objetos del aprendizaje básicos y alternos, asignándolos de acuerdo con el estilo de aprendizaje del estudiante.…”

Section: áReas De Desarrollounclassified

“…Otro aspecto importante son las redes semánticas que se desenvuelven en un rol importante para la representación de conocimiento (Gregg, 2007); es a través de estas que se generan los grafos conceptuales que dan apertura a la notación gráfica y en la automatización del enlace del lenguaje natural (Sowa, 1992), haciéndolo de manera lógica y precisa para fortalecer los procesos de cognición en el estudiante.…”

Section: Algoritmos De Aprendizajeunclassified

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Tendencias en el desarrollo de agentes inteligentes aplicados en la construcción de un aula inteligente

Lizcano¹,

García²,

Holguín³

2009

Rev. Investig. UNAD

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RESUMENLos agentes inteligentes o sistemas multiagente son una tendencia importante de investigación en Inteligencia Artificial. En esta línea de trabajo, el proyecto "Aula Inteligente", 15 desarrollado en colaboración con varios grupos de investigación, busca su aplicación en el apoyo a procesos de aprendizaje autónomo, en un aula digital. Aquí se presentan las conclusiones obtenidas en una de las primeras actividades de desarrollo del proyecto Aula Inteligente, que consistió en realizar un proceso de consulta bibliográfica sistemática que ha permitido identificar las tendencias, utilidad y tecnologías de agentes y sistemas multiagente, en el ámbito de procesos de aprendizaje de agentes naturales y artificiales. El artículo muestra la conceptualización fundamental sobre agentes inteligentes, la metodología utilizada para adelantar la revisión, los hallazgos obtenidos en el proceso y la aplicación de los mismos en diferentes aspectos del proyecto Aula Inteligente. ABSTRACTIntelligent agents or multiagent systems are a major trend of research in Artificial Intelligence. In this way of work, the "Aula Inteligente" project (Smart Classroom), developed in collaboration between several research groups, are seeking their support in the implementation processes of autonomous learning in a digital classroom. This paper presents the findings at an early project development activities, which has conducted a consultation process that the literature has identified systematic trends of development and use of agents and multiagent systems in learning processes of natural and artificial agents. This paper presents the fundamental concept of intelligent agents, the methodology used to advance the literature review, the findings obtained in the process and applying it in different aspects of the "Aula Inteligente" project.

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Section: áReas De Desarrollounclassified

Section: Algoritmos De Aprendizajeunclassified

Tendencias en el desarrollo de agentes inteligentes aplicados en la construcción de un aula inteligente

Lizcano¹,

García²,

Holguín³

2009

Rev. Investig. UNAD

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“…For implementing the elearning system there are many agents as described by Dawn G. Gregg in his paper [4]. These are Instruction Agent, Lesson Planning Agent, Resource Location Agent, Personalization Agent, Learner Centred Agent, and Collaboration Agent.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to Dawn G. Gregg following features should be provided by the learning system to user [4].…”

Section: Features Supported By An E-learning Systemmentioning

confidence: 99%

A Survey on Semantic Web based E-learning

KumarTiwari¹,

Chaudhary²,

Tomar³

2014

IJCA

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E-learning is method of distance learning using electronic media, in which learner and instructor interacts via learning content present on web. Semantic web is extension of current web. Semantic web attains importance now a day. For implementing the effective e-learning system, Semantic Web appears as a promising technology. Now, it becomes a need to use semantic nature of web in e-learning. Semantic web based e-learning is next generation of e-learning. This paper presents the different kind of e-learning models.

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“…In addition, intelligent agents in e-learning are suggested to provide learning content adapted to the needs and styles of learners (Chellatamilan & Suresh, 2011), and to customize learning (Alexandru, Tirziu, Tudora, & Bica, 2015;Baylari & Montazer, 2009;Gregg, 2007). Since these agents have already solved many problems in online learning, we focus on an evolving concept by adopting them to improve the performance of learners.…”

Section: Performance Optimization Approach For Online Learning Based mentioning

confidence: 99%

Intelligent Agents for Dynamic Optimization of Learner Performances in an Online System

2017

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Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners’ failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these vulnerabilities due to the lack of direct contact with the teacher, who would be able to point out the learner’s difficulties and help to rectify them. Methodology: The research sample was 49 learners enrolled in an online course. We focused on three cognitive factors: language, memory, and reasoning. We propose an approach to optimize learners’ performances based on two intelligent agents that model the role of a teacher: the “detector agent” and the “rectifier agent”. Contribution: The intelligent agents beneficially contribute to e-learning enrichment and the development of cognitive skills and solidification of knowledge acquisition. This is achieved by strengthening the memory, the assimilation of lessons by improving language skills, and the reinforcement of problem solving by developing reasoning and analysis capacity. Findings: The results show that the proposed approach efficiently detects the weaknesses of learners and resolves them intelligently. Future Research: The approach toward e-learning performance can be improved by focusing on other factors and intelligent agents that can improve the yield for learners and more effectively optimize system operation for their perceived needs.

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E‐learning agents

Cited by 39 publications

References 43 publications

Tendencias en el desarrollo de agentes inteligentes aplicados en la construcción de un aula inteligente

Tendencias en el desarrollo de agentes inteligentes aplicados en la construcción de un aula inteligente

A Survey on Semantic Web based E-learning

Intelligent Agents for Dynamic Optimization of Learner Performances in an Online System

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