Escriptura de la tesi doctoral, benestar i context d'aprenentatge: Un estudi des de les percepcions dels doctorands

Les representacions que els estudiants es fan sobre les tasques acadèmiques són cabdals per entendre com les desenvolupen. Creiem que això no és una excepció en estudiants de doctorat amb les seves tesis i és per això que en aquesta recerca estem interessats en investigar com els estudiants entenen els estudis de doctorat. La literatura revisada preocupada per l’experiència del doctorat, és a dir, com els doctorands perceben aquest procés, se centra en variables de benestar, context d’aprenentatge i escriptura. Amb el propòsit d’obtenir un quadre complert sobre com els doctorands entenen fer una tesi, 627 doctorands han completat El Qüestionari de l’Experiència Doctoral (Lonka i altres, 2007) que hem procedit a adaptar a la població espanyola. Aquest instrument mesura les tres variables esmentades (al llarg de 49 enunciats de resposta Likert) i de forma general algunes qüestions del procés doctoral (8 preguntes de resposta oberta) que complementen/donen llum a la interpretació de les dades quantitatives. A més, es demana informació del context del doctorand (18 preguntes) que ajuda a entendre millor el desenvolupament de la tesi en cada cas. Donat que algunes dificultats que els estudiants manifesten en el doctorat tenen a veure amb la percepció de no disposar d’estratègies suficients per regular el procés d’escriptura, ens hem plantejat recollir dades més específiques en relació a l’escriptura de la tesi entrevistant 10 doctorands per separat i posteriorment junts en un focus grup. Pensem que la nostra investigació pot contribuir en la reflexió de la qualitat dels programes de doctorat ja que creiem que els estudiants tenen molt a dir i que cal escoltar les seves veus. A més, si els tutors disposen d’informació sobre com els seus alumnes viuen els estudis de doctorat, segurament entendran millor com porten a terme les seves tesis i podran oferir-los ajudes més ajustades.

Solució mental i solució motriu en la iniciació als esports col·lectius en l'Educació Primària

The adjustment of the teaching learning contents in Physical Education, requires of a rigorous analysis that permits to adjust them to the maximum with the educational needs of the student body. It has been approached an investigation study in the one which have intervened pupils, boys and girls, of two different populations, Girona and Madrid in order to prove and analyse the motor and mental components of the student body in the real situation of the game. The hypothesis that we have treated are:if it exists differences between the boys and girls of the educational levels studied in the motor and mental solution in the sports initiation, additionally, the differences that they can exist between the courses and what distance is verified between the study ages to approach a physical activity that implies an initial step to the hour of teaching the collective sports in the classes of Physical education. They have been employed three measure instruments: the first permits to analyse the mental solution without need of practice employing situation photographs of the real game with those which the pupils must choose to who to happen; the second is a pass test that permits to prove the technical dominance to use in a collective sport and the third is a real game situation that permits to put in manifesto the relationship between the mental behaviour and the motor of the pupil. This real game situation is ‘the game of ten pass’ (Blázquez,1986; Torres,1993). The results demonstrate that it do not exist differences between the two sexes in the study ages. In the case of the technical execution level, there is a considerable increase with the age and it is slightly greater in the kids that in the girls. In the case of the real game, we find ourselves with a great variability in the results and we cannot conclude that there are relative differences to the sex in none of the three courses. Respect at participation level during the game is confirmed that the pupils that more participate are not the pupils than more balls lose, what permits to guarantee the idea of the fact that it is convenient to use the real game practice as direct learning element. Finally, there is no a high correlation between the execution level measured in the test of technical execution and the decision execution during the game

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

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

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

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

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

Visuo-motor and cognitive procedural learning in children with basal ganglia pathology.

We investigated procedural learning in 18 children with basal ganglia (BG) lesions or dysfunctions of various aetiologies, using a visuo-motor learning test, the Serial Reaction Time (SRT) task, and a cognitive learning test, the Probabilistic Classification Learning (PCL) task. We compared patients with early (<1 year old, n=9), later onset (>6 years old, n=7) or progressive disorder (idiopathic dystonia, n=2). All patients showed deficits in both visuo-motor and cognitive domains, except those with idiopathic dystonia, who displayed preserved classification learning skills. Impairments seem to be independent from the age of onset of pathology. As far as we know, this study is the first to investigate motor and cognitive procedural learning in children with BG damage. Procedural impairments were documented whatever the aetiology of the BG damage/dysfunction and time of pathology onset, thus supporting the claim of very early skill learning development and lack of plasticity in case of damage.