SISTEMAS TUTORES INTELIGENTES COMO APOYO EN EL PROCESO DE APRENDIZAJE

En los procesos de enseñanza-aprendizaje surgen diversos problemas en cuanto al entendimiento y la comprensión del conocimiento. Estas dificultades radican principalmente en que todas las personas tienen un estilo de aprendizaje diferente y los métodos clásicos de enseñanza no cubren sus necesidades particulares. El desarrollo de la tecnología ha impulsado la creación de herramientas que brindan una solución eficiente a dicha problemática: los Sistemas Tutores Inteligentes (STI). El objetivo principal de este artículo es la identificación de las principales características de estos tutores, haciendo énfasis en los beneficios que ofrece como apoyo en los procesos de enseñanza-aprendizaje en el contexto educativo. El método utilizado es el descriptivo y sistémico, el cual permite recopilar los datos necesarios. La investigación permitió reunir los aspectos más relevantes de los STI y presentarlos como una herramienta óptima para llevar a cabo un proceso de aprendizaje.Intelligent System Tutors as Support in the Learning ProcessAbstractIn the teaching and learning processes various problems arise as to the understanding and comprehension of knowledge. These difficulties are mainly in which everyone has a different way of learning and classic teaching methods do not meet your particular needs. The development of technology has led to the creation of tools that provides an efficient solution to this problem: Intelligent Tutoring Systems (ITS). The main objective of this article is to identify the main features of these tutors, emphasizing the benefits and support in the teaching-learning in the educational context. The method used is the descriptive and systemic, which allows you to collect the necessary data. The research brought together the most important aspects of ITS and present them as an excellent tool to perform a learning process.Keywords: intelligent applications, teaching strategies, Artificial Intelligence (IA), modules, knowledge.

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Enhancing Intelligent Tutoring Systems with the Agent Paradigm

Intelligent Tutoring Systems in E-Learning Environments ◽

10.4018/978-1-61692-008-1.ch003 ◽

2011 ◽

pp. 46-69 ◽

Cited By ~ 2

Author(s):

Xin Bai ◽

John B. Black

Keyword(s):

Learning Environment ◽

Intelligent Tutoring Systems ◽

Teaching And Learning ◽

Intelligent Tutoring ◽

Game Based Learning ◽

Tutoring Systems ◽

The Real ◽

Future Design ◽

Cognitive Framework ◽

Computer Based

A cognitive framework called REflective Agent Learning environment (REAL) is developed in this study. REAL is a reusable framework that allows researchers to develop a simulation-based learning environment where users can learn through passing their thoughts to some computer-based agents and observe how the agents embodying their knowledge behave as the result of their instruction. Our research benefits from the research in Intelligent Tutoring Systems, game based learning systems, and agent technologies, stressing reflection as part of the thinking processes. It focuses on the design of the framework and the testing of its usability. The external evaluation of specific implementations serves as the guidance for the future design of the REAL applications. We hope, by grounding themselves in the needs of local practice, the REAL applications can give us opportunities to understand how theoretical claims about teaching and learning can be effectively transformed into meaningful learning.

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Data-Driven Intelligent Tutoring Systems for STEM+C Learning and Teaching

European Journal of Engineering Research and Science ◽

10.24018/ejers.2019.4.9.1553 ◽

2019 ◽

Vol 4 (9) ◽

pp. 202-206

Author(s):

Hieu Trong Bui

Keyword(s):

Problem Solving ◽

Intelligent Tutoring Systems ◽

Student Behavior ◽

Teaching And Learning ◽

Intelligent Tutoring ◽

Data Driven ◽

Tutoring Systems ◽

Learning And Teaching ◽

Past Data ◽

Problem Solving Behavior

It is wide known that one of the most effective ways to learn is through problem solving. In recent years, it is widely known that problem solving is a central subject and fundamental ability in the teaching and learning. Besides, problem solving is integrated in the STEM+C (Science, Technology, Engineering, and Math plus Computing, Coding or Computer Science) fields. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. Intelligent tutoring systems provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. In this work, the main goal of this paper is to: 1) present ITSs used in the STEM+C education; and 2) introduce data-driven ITSs for STEM+C education.

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