Exploring the role of market learning capability in competitive strategy

It has been argued that a firm's capacity to learn from its market is a source of both innovation and competitive advantage. However, past research has failed to conceptualize market-focused learning activity as a capability having the potential to contribute to competitive advantage. Prior innovation research has been biased toward technological innovation. However, there is evidence to suggest that both technological and non-technological innovations contribute to competitive advantage reflecting the need for a broader conceptualization of the innovation construct. Past research has also overlooked the critical role of entrepreneurship in the capability building process. Competitive advantage has been predominantly measured in terms of financial indicators of performance. In general, the literature reflects the need for comprehensive measures of organizational innovation and competitive advantage. This paper examines the role of market-focused learning capability in organizational innovation-based competitive strategy. The paper contributes to the strategic marketing theory by developing and refining measures of entrepreneurship, market-focused learning capability, organizational innovation and sustained competitive advantage, testing relationships among these constructs.

Using Modern Tools to Explain the Use of the Smith Chart

The concept of explaining the use of an old tool like the Smith chart, using modern tools like MATLAB [1] scripts in combination with e-learning facilities, is exemplified by two MATLAB scripts. These display, step by step, the graphical procedure that must be used to solve the double-stub impedance-matching problem. These two scripts correspond to two different possible ways to analyze this matching problem, and they are important for students to learn by themselves.

Accounting education in an action-based learning environment: lessons learned

In the context of the Bologna Declaration a change is taking place in the teaching/learning paradigm. From teaching-centered education, which emphasizes the acquisition and transmission of knowledge, we now speak of learning-centered education, which is more demanding for students. This paradigm promotes a continuum of lifelong learning, where the individual needs to be able to handle knowledge, to select what is appropriate for a particular context, to learn permanently and to understand how to learn in new and rapidly changing situations. One attempt to face these challenges has been the experience of ISCAP regarding the teaching/learning of accounting in the course Managerial Simulation. This paper describes the process of teaching, learning and assessment in an action-based learning environment. After a brief general framework that focuses on education objectives, we report the strengths and limitations of this teaching/learning tool. We conclude with some lessons from the implementation of the project.

Knowledge sharing and learning processes: case study of portuguese technological schools and higher education institutions

Paper to be presented at the ESREA Conference Learning to Change? The Role of Identity and Learning Careers in Adult Education, 7-8 December, 2006, Université Catholique Louvain, Louvain–la-Neuve, Belgium

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

A mobile robot platform for open learning based on serious games and remote laboratories

Within the pedagogical community, Serious Games have arisen as a viable alternative to traditional course-based learning materials. Until now, they have been based strictly on software solutions. Meanwhile, research into Remote Laboratories has shown that they are a viable, low-cost solution for experimentation in an engineering context, providing uninterrupted access, low-maintenance requirements, and a heightened sense of reality when compared to simulations. This paper will propose a solution where both approaches are combined to deliver a Remote Laboratory-based Serious Game for use in engineering and school education. The platform for this system is the WebLab-Deusto Framework, already well-tested within the remote laboratory context, and based on open standards. The laboratory allows users to control a mobile robot in a labyrinth environment and take part in an interactive game where they must locate and correctly answer several questions, the subject of which can be adapted to educators' needs. It also integrates the Google Blockly graphical programming language, allowing students to learn basic programming and logic principles without needing to understand complex syntax.

A criança invisual é tão apta à aprendizagem como as restantes

Relatório de Estágio apresentado à Escola Superior de Educação de Lisboa para obtenção do grau de mestre em Ensino do 1.º e 2.º Ciclo do Ensino Básico

Modelling an e-learning environment for learning programming languages

It is widely accepted that solving programming exercises is fundamental to learn how to program. Nevertheless, solving exercises is only effective if students receive an assessment on their work. An exercise solved wrong will consolidate a false belief, and without feedback many students will not be able to overcome their difficulties. However, creating, managing and accessing a large number of exercises, covering all the points in the curricula of a programming course, in classes with large number of students, can be a daunting task without the appropriated tools working in unison. This involves a diversity of tools, from the environments where programs are coded, to automatic program evaluators providing feedback on the attempts of students, passing through the authoring, management and sequencing of programming exercises as learning objects. We believe that the integration of these tools will have a great impact in acquiring programming skills. Our research objective is to manage and coordinate a network of eLearning systems where students can solve computer programming exercises. Networks of this kind include systems such as learning management systems (LMS), evaluation engines (EE), learning objects repositories (LOR) and exercise resolution environments (ERE). Our strategy to achieve the interoperability among these tools is based on a shared definition of programming exercise as a Learning Object (LO).

A learning environment for robotic production line simulation

This paper presents a framework for a robotic production line simulation learning environment using Autonomous Ground Vehicles (AGV). An eLearning platform is used as interface with the simulator. The objective is to introduce students to the production robotics area using a familiar tool, an eLearning platform, and a framework that simulates a production line using AGVs. This framework allows students to learn about robotics but also about several areas of industrial management engineering without requiring an extensive prior knowledge on the robotics area. The robotic production line simulation learning environment simulates a production environment using AGVs to transport materials to and from the production line. The simulator allows students to validate the AGV dynamics and provides information about the whole materials supplying system which includes: supply times, route optimization and inventory management. The students are required to address several topics such as: sensors, actuators, controllers and an high level management and optimization software. This simulator was developed with a known open source tool from robotics community: Player/Stage. This tool was extended with several add-ons so that students can be able to interact with a complex simulation environment. These add-ons include an abstraction communication layer that performs events provided by the database server which is programmed by the students. An eLearning platform is used as interface between the students and the simulator. The students can visualize the effects of their instructions/programming in the simulator that they can access via the eLearning platform. The proposed framework aims to allow students from different backgrounds to fully experience robotics in practice by suppressing the huge gap between theory and practice that exists in robotics. Using an eLearning platform eliminates installation problems that can occur from different computers software distribution and makes the simulator accessible by all students at school and at home.

Ontologies learn by searching

Dissertation to obtain the Master degree in Electrical Engineering and Computer Science

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Problemáticas de la interacción discursiva en la enseñanza y el aprendizaje de las Ciencias Naturales. Aportes didácticos. Interaction discoursive issues in teaching and learning Natural Sciences. Didactic contributions

Las didácticas específicas de las ciencias naturales revelan diferentes problemáticas en su enseñanza y aprendizaje en los diferentes niveles del sistema educativo. En particular, en las clases de ciencias la interacción discursiva docente alumnos adquiere relevancia, ya que el proceso de comunicación del conocimiento es uno de los pilares didácticos, junto a la trasposición del mismo. Especificamente, en este proyecto nos abocamos a aquellas intervenciones de docentes y alumnos que se relacionan con la construcción del conocimiento biológico y químico. El proyecto se enmarca en una actual linea de trabajo que indaga sobre las dificultades en los abordajes del conocimiento científico en las aulas, las características del discurso entre docentes y alumnos, las habilidades y dificultades en la comprensión de los enunciados de problemas y las características de los textos que se utilizan en las clases. Se focaliza este estudio en casos que intentan dar respuesta a tres temáticas, agrupadas en un conjunto de situaciones de investigación relacionadas con la interacción discursiva docente-alumno, retomando el rol del docente al hablar, guiar o diseñar las situaciones de referencia para el aprendizaje de los alumnos. Los casos son: 1- En cuanto a las concepciones sobre diversidad biológica en estudiantes de escuela secundaria y en textos académicos, atendemos a cómo la escuela presenta los contenidos ecológicos como un conjunto de dogmas y conceptos estáticos. Además suelen simplificarse conceptualmente y presentarse poco actualizados. Es por ello que se planea estudiar las concepciones y actitudes de los alumnos de secundaria sobre la biodiversidad, cómo estas dificultan su comprensión y los textos usados en relación a la promoción de la transposición didáctica. 2- En relación a cómo se elabora el patrón temático del tema célula en clases de Biología, se analizarán las diferentes estrategias de significados y de desarrollo temático, que se emplean en la comunicación aulica. Se intentará establecer si hay cambios en el desarrollo temático a medida que se avanza en la escolaridad. Esto es porque se puede apreciar que muchos de los problemas de aprendizaje del alumnado se deben a un desconocimiento tanto del patrón temático como del patrón estructural de la ciencia, siendo preciso evocar los patrones temáticos que se quieren utilizar, para construir un conocimiento compartido. 3-Finalmente, en los enunciados de problemas de Química, se analizarán las dificultades de comprensión lectora de alumnos de Ingeniería. Los docentes frecuentemente atribuyen los problemas a deficiencias en la instrucción recibida, sin considerarse los conocimientos previos del alumno, los obstáculos conceptuales originados en el tema, las deficiencias en la habilidad lectora, el tipo textual predominante en la consigna, el formato en el que se escribió la consigna y los factores personales, etc., siendo que la comprensión del enunciado de una consigna de trabajo condiciona fuertemente la posibilidad de su resolución. Los tres casos utilizarán metodologías cualitaritas que incluyan análisis de contenido en discursos orales y escritos. Los datos se registrarán desde observación no participante, registro etnográfico y con grabaciones de audio. Se espera contribuir al conocimiento, realizando aportes a la formación docente en tanto las estrategias discursivas que se emplean en el aula, en forma oral y en la escrita, conocer concepciones que dificultan o favoren la construcción del conocimiento científico, entre otras. Los productos de estos estudios estarán integrados por nuevos desarrollos para la formación docente, publicaciones científicas de impacto nacional e internacional, presentaciones a congresos, materiales didácticos y divulgativos, dictado de seminarios y/o cursos, redacción de informes a las escuelas intervinientes.. The specific Natural Sciences didactics show different problems in teaching and learning along the school system. In particular, the discourse used to communicate knowledge in Science lessons becomes important. With this project we will focus on the teachers and students actions regarding the construction of biological and chemical knowledge. This project attempts to answer these issues and brings together a range of research situations related to teacher-student interaction, through discourse, taking up the role of the teacher to speak, to plan and to guide student learning. We will study the ideas and attitudes of high school students about biodiversity that make difficult its understanding and the textbooks used in relation to promotion of the didactic transposition. In addition, regarding how the thematic pattern in biology classes is costructed, it will be analyzed the different meaning and thematic development strategies that are used in communication. We will attempt to establish whether there are any changes in the thematic development throughout high school education. Finally, we will analyze the reading comprehension problems in engineering students. Teachers frequently attribute these issues to deficiencies in prior education, without considering the students background, the conceptual obstacles arising in the field, the format in which the prompt is written, personal factors, etc., keeping in mind that the outcome of an activity is strictly dependant con the prompt understanding. We expect to make contributions to the teacher education in both the discourse strategies used in the classroom, orally and in writing, to learn about the conceptions that hinder or favor the knowledge construction, among others. The products of this study will be national and international impact scientific publications, conference presentations, popular science publications, seminars courses and reports to the schools involeved.

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 capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.