Investigating independent language learning in classroom-based EFL Learners

La recerca efectuada sobre les estrat��gies d���aprenentatge de lleng��es ha demostrat que els aprenents que utilitzen estrat��gies metacognitives (planificaci��, revisi�� i avaluaci��) desenvolupen estrat��gies cognitives m��s eficaces (Anderson, 2002). Aquest article descriu les activitats que 43 estudiants de llengua estrangera de la Universitat de Vic van emprendre de forma independent i dedueix les estrat��gies metacognitives que van utilitzar sense cap formaci�� pr��via en estrat��gies. Els estudiants van completar un dossier on expressaven les necessitats d���aprenentatge, la planificaci�� i supervisi�� de les activitats i finalment l���avaluaci�� de l���aprenentatge que havien portat a terme de manera independent fora de les hores lectives. La primera fase de l���an��lisi de les dades revela que, tot i que els estudiants foren capa��os d���expressar les necessitats d���aprenentatge en general, la formulaci�� d���objectius i la supervisi�� de les activitats fou escassa. La discussi�� gira entorn de la formaci�� dels estudiants de lleng��es estrangeres en estrat��gies metacognitives i la integraci�� de l���aprenentatge aut��nom dins el curr��culum docent.

Stock market volatility and learning

Introducing bounded rationality in a standard consumption-based asset pricing model with time separable preferences strongly improves empirical performance. Learning causes momentum and mean reversion of returns and thereby excess volatility, persistence of price-dividend ratios, long-horizon return predictability and a risk premium, as in the habit model of Campbell and Cochrane (1999), but for lower risk aversion. This is obtained, even though our learning scheme introduces just one free parameter and we only consider learning schemes that imply small deviations from full rationality. The findings are robust to the learning rule used and other model features. What is key is that agents forecast future stock prices using past information on prices.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene��fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor��tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn��ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo��lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ��ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se��lection shapes animals' cognitive abilities. - La capacit�� d'apprendre �� associer des sensations perceptives �� des actions motrices appropri��es est sous-jacente au succ��s ��volutif de nombreuses esp��ces, depuis les insectes jusqu'aux ��tres hu��mains. L'importance ��volutive de l'apprentissage est depuis longtemps un sujet d'int��r��t pour les biologistes de l'��volution, et ces derniers mettent l'accent sur le b��n��fice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est n��cessaire de passer de mani��re flexible d'un comportement �� l'autre. Cependant, deux questions non r��solues sont importantes afin d'am��liorer notre savoir quant aux avantages ��volutifs procur��s par l'apprentissage. Premi��rement, puisqu'il est possible d'apprendre un comportement incorrect quand une t��che est trop complexe, les r��gles d'apprentissage qui permettent d'atteindre un com��portement r��ellement adaptatif doivent ��tre identifi��es avec une plus grande pr��cision, et doivent ��tre mises en relation avec les probl��mes ��cologiques sp��cifiques rencontr��s par chaque esp��ce. Un cadre th��orique ayant pour but de pr��dire le comportement �� partir de la d��finition d'une r��gle d'apprentissage est d��velopp�� ici. Il est d��montr�� que les caract��ristiques cognitives, telles que la tendance �� explorer ou la capacit�� d'inf��rer les r��compenses li��es �� des actions non ex��p��riment��es, interagissent de mani��re non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces pr��dictions comportementales sont utilis��es dans un mod��le ��volutif afin de d��montrer que, de mani��re surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage bas�� sur l'inf��rence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxi��me question quant �� l'��volution de l'apprentissage concerne son lien et son avantage relatif vis-��-vis d'autres formes plus simples de plasticit�� ph��notypique. Apr��s avoir clarifi�� la distinction entre r��ponses aux stimuli g��n��tiquement d��termin��es ou apprises, un nouveau fac��teur favorisant l'��volution de l'apprentissage est propos�� : la complexit�� environnementale. Un mod��le math��matique permet de montrer qu'une mesure de la complexit�� environnementale - le nombre de stimuli rencontr��s dans l'environnement - a un r��le fondamental pour l'��volution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant �� la mo��d��lisation des interactions entre les esp��ces en ��volution et leur environnement, dans le but de comprendre comment la s��lection naturelle fa��onne les capacit��s cognitives des animaux.

PERSONAL(ONTO) : Personalizing the Learning Process in Virtual Environments by means of Adaptive Formative Itineraries based on Reusable Learning Objects and Ontologies TIN2006-15107-C02

Personalization in e-learning allows the adaptation of contents, learning strategiesand educational resources to the competencies, previous knowledge or preferences of the student. This project takes a multidisciplinary perspective for devising standards-based personalization capabilities into virtual e-learning environments, focusing on the conceptof adaptive learning itinerary, using reusable learning objects as the basis of the system and using ontologies and semantic web technologies.

Using problem-based learning for introducing producer theory and market structure in intermediate microeconomics

This paper shows how instructors can use the problem���based learning method to introduce producer theory and market structure in intermediate microeconomics courses. The paper proposes a framework where different decision problems are presented to students, who are asked to imagine that they are the managers of a firm who need to solve a problem in a particular business setting. In this setting, the instructors��� role isto provide both guidance to facilitate student learning and content knowledge on a just���in���time basis

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Recommender System Based on Consumer Product Reviews

Consumer reviews, opinions and shared experiences in the use of a product is a powerful source of information about consumer preferences that can be used in recommender systems. Despite the importance and value of such information, there is no comprehensive mechanism that formalizes the opinions selection and retrieval process and the utilization of retrieved opinions due to the difficulty of extracting information from text data. In this paper, a new recommender system that is built on consumer product reviews is proposed. A prioritizing mechanism is developed for the system. The proposed approach is illustrated using the case study of a recommender system for digital cameras

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

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

A context-based methodology for integrating Web 2.0 services in learning scenarios

The emergence of the Web 2.0 technologies in the last years havechanged the way people interact with knowledge. Services for cooperation andcollaboration have placed the user in the centre of a new knowledge buildingspace. The development of new second generation learning environments canbenefit from the potential of these Web 2.0 services when applied to aneducational context. We propose a methodology for designing learningenvironments that relates Web 2.0 services with the functional requirements ofthese environments. In particular, we concentrate on the design of the KRSMsystem to discuss the components of this methodology and its application.

Discovering the campus together: a mobile and computer-based learning experience

One of the most relevant difficulties faced by first-year undergraduate students is to settle into the educational environment of universities. This paper presents a case study that proposes a computer-assisted collaborative experience designed to help students in their transition from high school to university. This is done by facilitating their first contact with the campus and its services, the university community, methodologies and activities. The experience combines individual and collaborative activities, conducted in and out of the classroom, structured following the Jigsaw Collaborative Learning Flow Pattern. A specific environment including portable technologies with network and computer applications has been developed to support and facilitate the orchestration of a flow of learning activities into a single integrated learning setting. The result is a Computer-Supported Collaborative Blended Learning scenario, which has been evaluated with first-year university students of the degrees of Software and Audiovisual Engineering within the subject Introduction to Information and Communications Technologies. The findings reveal that the scenario improves significantly students��� interest in their studies and their understanding about the campus and services provided. The environment is also an innovative approach to successfully support the heterogeneous activities conducted by both teachers and students during the scenario. This paper introduces the goals and context of the case study, describes how the technology was employed to conduct the learning scenario, the evaluation methods and the main results of the experience.

Collage, a Collaborative Learning Design Editor Based on Patterns

This paper introduces Collage, a high-level IMS-LD compliant authoring tool that is specialized for CSCL (Computer-Supported Collaborative Learning). Nowadays CSCL is a key trend in elearning since it highlights the importance of social interactions as an essential element of learning. CSCL is an interdisciplinary domain, which demands participatory design techniques that allow teachers to get directly involved in design activities. Developing CSCL designs using LD is a difficult task for teachers since LD is a complex technical specification and modelling collaborative characteristics can be tricky. Collage helps teachers in the process of creating their own potentially effective collaborative Learning Designs by reusing and customizing patterns, according to the requirements of a particular learning situation. These patterns, called Collaborative Learning Flow Patterns (CLFPs), represent best practices that are repetitively used by practitioners when structuring the flow of (collaborative) learning activities. An example of an LD that can be created using Collage is illustrated in the paper. Preliminary evaluation results show that teachers, with experience in CL but without LD knowledge, can successfully design real collaborative learning experiences using Collage.

A multicase study for the evaluation of a pattern-based visual design process for collaborative learning

Collage is a pattern-based visual design authoring tool for the creation of collaborative learning scripts computationally modelled with IMS Learning Design (LD). The pattern-based visual approach aims to provide teachers with design ideas that are based on broadly accepted practices. Besides, it seeks hiding the LD notation so that teachers can easily create their own designs. The use of visual representations supports both the understanding of the design ideas and the usability of the authoring tool. This paper presents a multicase study comprising three different cases that evaluate the approach from different perspectives. The first case includes workshops where teachers use Collage. A second case implies the design of a scenario proposed by a third-party using related approaches. The third case analyzes a situation where students follow a design created with Collage. The cross-case analysis provides a global understanding of the possibilities and limitations of the pattern-based visual design approach.