A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Evolució i reptes de l'educació virtual : construint l'e-learning del segle XXI

Les autores i autors d'aquest llibre ofereixen una descripció de l'evolució general de l'e-learning apuntant els elements clau cap als que ha d'anar evolucionant. Parteixen de l'experiència i la pràctica contrastada amb les investigacions sobre el tema. Al llarg dels diferents capítols ens mostren com viu un estudiant virtual, el seu paper i la manera com planteja i organitza les seves activitats; ens acosten al professorat analitzant el seu rol en el disseny de la formació i la comunicació amb els estudiants; parlen de la col·laboració, analitzant com dissenyar activitats col·laboratives i assenyalant els seus avantatges i límits; descriuen els diferents recursos d'aprenentatge que podem disposar en el disseny dels cursos i, finalment, acompanyen la nostra mirada cap al futur pròxim analitzant les tendències i els reptes als que hem de fer front per construir l'e-learning del segle XXI.

Testing Delivery Systems in Transnational virtual learning: the vocational management training for the european tourism industry (VocMat) case study

This article discusses the lessons learned from developing and delivering the Vocational Management Training for the European Tourism Industry (VocMat) online training programme, which was aimed at providing flexible, online distance learning for the European tourism industry. The programme was designed to address managers ‘need for flexible, senior management level training which they could access at a time and place which fitted in with their working and non-work commitments. The authors present two main approaches to using the Virtual Learning Environment, the feedback from the participants, and the implications of online Technology in extending tourism training opportunities

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

Beyond the Transatlantic Divide: The Multiple Authorities of Standards in the Global Political Economy of Services

This paper explores the plurality of institutional environments in which standards for the service sector are expected to support the rise of a global knowledge-based economy. Despite the careful wording of the World Trade Organization (WTO), a whole range of international bodies still have the capacity to define technical specifications affecting how services are expected to be traded on worldwide basis. The analysis relies on global political economy approaches to extend to the area of service standards the assumption that the process of globalization is not opposing states and markets, but a joint expression of both of them including new patterns and agents of structural change through formal and informal power and regulatory practices. It analyses on a cross-institutional basis patterns of authority in the institutional setting of service standards in the context of the International Organisation for Standardisation (ISO), the European Union, and the United States. In contrast to conventional views opposing the American system to the ISO/European framework, the paper questions the robustness of this opposition by showing that institutional developments of service standards are likely to face trade-offs and compromises across those systems and between two opposing models of standardisation.

Current standards and progress in understanding and treatment of GIST.

Gastrointestinal stromal tumours (GIST), despite being rare, pose a relevant medical problem from the viewpoint of diagnosis and management. GIST are fragile, liable to metastasize and often located in delicate structures. Surgical options, therefore, are limited. In the last decade an improved understanding of the molecular mechanisms of the disease has resulted in novel modes of treatment. The introduction of systemic tyrosine kinase inhibitor therapy with imatinib has significantly improved the outcome of the disease and prolonged the survival of GIST patients. For many patients the acute threat of a deadly cancer has been transformed into a manageable chronic condition. Drug safety, tolerability and compliance, subjects of concern in all long-term therapies, have proven to be acceptable for the tyrosine kinase inhibitor imatinib. The present paper provides a compact overview of the epidemiology, pathophysiology and morphology of GIST, with special reference to the underlying molecular biology. Relevant aspects of diagnosis, therapy and monitoring of the disease are reviewed with particular emphasis on the available clinical evidence and recent guidelines.

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

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

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