Designing context-aware adaptive units of learning based on IMS-LD standard

Learning contents adaptation has been a subject of interest in the research area of the adaptive hypermedia systems. Defining which variables and which standards can be considered to model adaptive content delivery processes is one of the main challenges in pedagogical design over e-learning environments. In this paper some specifications, architectures and technologies that can be used in contents adaptation processes considering characteristics of the context are described and a proposal to integrate some of these characteristics in the design of units of learning using adaptation conditions in a structure of IMS-Learning Design (IMS-LD) is presented. The key contribution of this work is the generation of instructional designs considering the context, which can be used in Learning Management Systems (LMSs) and diverse mobile devices

The Role of the online learner: onsite students becoming online learners

As universities are offering tuition through online learning environments, “onsite students” in higher education are increasingly becoming “online learners”. Since the medium for learning (and teaching) online is a digital environment, and at a distance, the role taken by students and teaching staff is different to the one these are used to in onsite, traditional settings. Therefore the Role of the Online Learner, presented in this paper, is key to onsite students who are to become online learners. This role consists of five competences: Operational, Cognitive, Collaborative, Self-directing, Course-specific. These five competences integrate the various skills, strategies, attitudes and awareness that make up the role of online learner, which learners use to perform efficiently online. They also make up the basis of a tutorial for would-be online learners, going over the Role of the Online Learner by means of concepts, examples and reflective activities. This tutorial, available to students in the author’s website, is also helpful to teaching and counselling staff in guiding their students to become online learners

Entorno de aprendizaje virtual adaptativo soportado por un modelo de usuario integral

El objetivo de esta tesis es mejorar la efectividad y eficiencia de los entornos de aprendizaje virtual. Para lograr este propósito se define un Modelo de Usuario que considera las características del usuario, el contexto y la Interacción. Estas tres dimensiones son integradas en un Modelo de Usuario Integral (MUI) para proveer adaptación de contenido, formato y actividades en entornos educativos con heterogeneidad de usuarios, tecnologías e interacciones. Esta heterogeneidad genera la entrega de contenidos, formatos y actividades inadecuadas para los estudiantes. La particularización del MUI en un entorno educativo es definida Modelo de Estudiante Integral (MEI). Las principales aportaciones de esta tesis son la definición y validación de un MUI, la utilización de un MEI abierto para propiciar la reflexión de los estudiantes sobre sus procesos de aprendizaje, la integración tecnológica con independencia de plataforma y la validación del MEI con estudiantes en escenarios reales.

Enhancing learning beyond the classroom: blogging and other social networking tools

Blogging has become one of the key ingredients of the so-called socials networks. This phenomenon has indeed invaded the world of education. Connections between people, comments on each other posts, and assessment of innovation are usually interesting characteristics of blogs related to students and scholars. Blogs have become a kind of new form of authority, bringing about (divergent) discussions which lead to creation of knowledge. The use of blogs as an innovative, educational tool is not at all new. However, their use in universities is not very widespread yet. Blogging for personal affairs is rather commonplace, but blogging for professional affairs – teaching, research and service, is scarce, despite the availability of ready-to-use, free tools. Unfortunately, Information Society has not reached yet enough some universities: not only are (student) blogs scarcely used as an educational tool, but it is quite rare to find a blog written by University professors. The Institute of Computational Chemistry of the University of Girona and the Department of Chemistry of the Universitat Autònoma de Barcelona has joined forces to create “InnoCiència”, a new Group on Digital Science Communitation. This group, formed by ca. ten researchers, has promoted the use of blogs, twitters. wikis and other tools of Web 2.0 in activities in Catalonia concerning the dissemination of Science, like Science Week, Open Day or Researchers’ Night. Likewise, its members promote use of social networking tools in chemistry- and communication-related courses. This communication explains the outcome of social-network experiences with teaching undergraduate students and organizing research communication events. We provide live, hands-on examples and interactive ground to show how blogs and twitters can be used to enhance the yield of teaching and research. Impact of blogging and other social networking tools on the outcome of the learning process is very depending on the target audience and the environmental conditions. A few examples are provided and some proposals to use these techniques efficiently to help students are hinted

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