Enseñar a aprender a pensar en los centros educativos, incluso en las actividades de evaluación.= Teaching to learn how to think at schools, even within assessment activities

El auténtico protagonismo de los centros educativos tiene que dirigirse a ayudar a pensar a sus alumnos y a enseñarlos a aprender, es decir, el docente tiene que enseñar estrategias de aprendizaje y debe promover el esfuerzo del estudiante para facilitar la construcción de esquemas y el aprendizaje permanente. El profesor debe utilizar cualquier situación de aprendizaje para enseñar dichas estrategias de aprendizaje, incluso en las situaciones de evaluación; por lo tanto, en este trabajo se sugiere que en las evaluaciones de los alumnos y alumnas se tenga en cuenta la metacognición como factor fundamental en el aprendizaje y la enseñanza

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Curriculum sequencing for an e-learning system based on learning styles

This work shows the use of adaptation techniques involved in an e-learning system that considers students' learning styles and students' knowledge states. The mentioned e-learning system is built on a multiagent framework designed to examine opportunities to improve the teaching and to motivate the students to learn what they want in a user-friendly and assisted environment

La Autogestión del aprendizaje y la autonomía e iniciativa personal = The self-regulation of learning and autonomy and personal Initiative

En esta investigación se trata de vincular la autogestión del aprendizaje con el desarrollo de la autonomía personal, la cual favorece el aprendizaje a lo largo de la vida. Partimos, en este proyecto de investigación, de la hipótesis de que la autogestión del aprendizaje es un elemento multidimensional cuya mejora revierte de forma positiva en el desarrollo de la autonomía e iniciativa personal. En esta comunicación se presentan los resultados obtenidos en la aplicación de la fase piloto de un proyecto más amplio, en el que se ha recogido información a través de autoinforme sobre las distintas estrategias y aspectos de la autogestión del aprendizaje y sobre la autonomía e iniciativa personal. Los resultados indican que, en general, hay una relación significativa positiva moderada entre las estrategias de aprendizaje y la autonomía, lo que confirma la importancia de ambos aspectos para favorecer el desarrollo integral de los estudiantes. El fomento de las estrategias de aprendizaje hace que los estudiantes desarrollen la autonomía. A su vez, exponer a los estudiantes a situaciones de aprendizaje que fomenten su autonomía mejora su competencia para aprender