Conception et évaluation d’une intervention didactique à propos des phases de la Lune dans un planétarium numérique

Depuis l’entrée en vigueur du Programme de formation de l’école québécoise en 2001, l’astronomie est à nouveau enseignée dans les classes du Québec. Malheureusement, l’école est mal outillée pour enseigner des concepts astronomiques complexes se déroulant pour la plupart en dehors des heures de classe et sur de longues périodes de temps. Sans compter que bien des phénomènes astronomiques mettent en jeu des astres se déplaçant dans un espace tridimensionnel auquel nous n’avons pas accès depuis notre point de vue géocentrique. Les phases de la Lune, concept prescrit au premier cycle du secondaire, sont de ceux-là. Heureusement, l’école peut compter sur l’appui du planétarium, musée de sciences dédié à la présentation, en accéléré et à toute heure du jour, de simulations ultra réalistes de divers phénomènes astronomiques. Mais quel type de planétarium secondera l’école ? Récemment, les planétariums ont eux aussi subi leur propre révolution : ces institutions sont passées de l’analogique au numérique, remplaçant les projecteurs optomécaniques géocentriques par des projecteurs vidéo qui offrent la possibilité de se déplacer virtuellement dans une simulation de l’Univers tridimensionnel complètement immersive. Bien que la recherche en éducation dans les planétariums se soit peu penchée sur ce nouveau paradigme, certaines de ses conclusions basées sur l’étude des planétariums analogiques peuvent nous aider à concevoir une intervention didactique fructueuse dans ces nouveaux simulateurs numériques. Mais d’autres sources d’inspiration seront invoquées, au premier chef la didactique des sciences, qui conçoit l’apprentissage non plus comme la transmission de connaissances, mais plutôt comme la construction de savoirs par les apprenants eux-mêmes, avec et contre leurs conceptions premières. La conception d’environnements d’apprentissages constructivistes, dont le planétarium numérique est un digne représentant, et l’utilisation des simulations en astronomie, complèteront notre cadre théorique et mèneront à la conception d’une intervention didactique à propos des phases de la Lune dans un planétarium numérique s’adressant à des élèves âgés de 12 à 14 ans. Cette intervention didactique a été mise à l’essai une première fois dans le cadre d’une recherche de développement (ingénierie didactique) visant à l’améliorer, à la fois sur son versant théorique et sur son versant pratique, par le biais de multiples itérations dans le milieu « naturel » où elle se déploie, ici un planétarium numérique gonflable de six mètres de diamètre. Nous présentons les résultats de notre première itération, réalisée en compagnie de six jeunes de 12 à 14 ans (quatre garçons et deux filles) dont nous avons recueilli les conceptions à propos des phases de la Lune avant, pendant et après l’intervention par le biais d’entrevues de groupe, questionnaires, mises en situation et enregistrement des interventions tout au long de l’activité. L'évaluation a été essentiellement qualitative, basée sur les traces obtenues tout au long de la séance, en particulier sous la voûte du planétarium. Ce matériel a ensuite été analysé pour valider les concepts théoriques qui ont mené à la conception de l'intervention didactique, d'une part, mais aussi pour faire émerger des améliorations possibles visant à bonifier l'intervention. Nous avons ainsi constaté que l'intervention provoque effectivement l'évolution des conceptions de la majorité des participants à propos des phases de la Lune, mais nous avons également identifié des façons de rendre l’intervention encore plus efficace à l’avenir.

Proposition théorique constructiviste du développement de la compétence culturelle infirmière

Dans un contexte de mondialisation, les frontières géographiques et politiques se font de plus en plus diffuses et donnent lieu à un mélange des cultures tant au niveau local qu'international. Ce pluralisme culturel observé dans la population se transpose dans les milieux de soins, amenant son lot d'enjeux et de défis pour la pratique et la formation infirmière. Le développement de la compétence culturelle chez les professionnels de la santé est considéré comme l'une des solutions favorisant la qualité et l'équité dans les soins en contexte de diversité culturelle. La compétence culturelle fait l'objet de nombreux articles scientifiques en sciences infirmières, mais bon nombre d'entre eux sont issus d'une perspective essentialiste. À notre connaissance, aucune étude ne permet de représenter la trajectoire de développement de cette compétence sur un continuum intégrant des apprentissages réalisés à la fois chez des étudiantes et des infirmières selon une perspective constructiviste. Cette étude vise donc à formuler une proposition théorique constructiviste du développement de la compétence culturelle infirmière. L'approche de théorisation ancrée de Corbin et Strauss (2008) a permis de documenter le processus de développement de la compétence culturelle chez des infirmières et des étudiantes dans un Centre de santé et de services sociaux desservant une population qui présente une grande diversité culturelle. Une stratégie d'échantillonnage intentionnel a permis de recruter des infirmières identifiées par leurs pairs comme étant expertes du domaine des soins en contexte de diversité culturelle, des infirmières se disant intéressées par une pratique culturellement compétente et des étudiantes en dernière année d'un programme de baccalauréat en sciences infirmières. Un total de 24 participantes, dont 13 infirmières et 11 étudiantes ont pris part à cette étude. Un questionnaire sociodémographique, des périodes d'observation participante et des entrevues semi-structurées ont servi d'outils de collecte des données. La catégorie centrale « apprendre à réunir les différentes réalités afin d'offrir des soins efficaces en contexte de diversité culturelle » a été construite à partir d'une analyse inductive des données. Cette catégorie centrale se divise en trois sous-catégories : « construire la relation avec l'autre », « sortir du cadre habituel de pratique » et « réinventer sa pratique dans l'action ». La proposition théorique formulée présente l'évolution concomitante de ces trois sous-catégories en trois niveaux de développement de la compétence culturelle infirmière : « s'ouvrir aux différentes réalités entourant la pratique en contexte de diversité culturelle », « mettre à l'épreuve sa pratique » et « réunir les différentes réalités de la pratique en contexte de diversité culturelle de façon intégrée ». La proposition théorique constructiviste est ancrée dans les données empiriques, circonscrit des étapes de développement interreliées et met en contexte les apprentissages du début du développement de la compétence culturelle à l'expertise. Les éléments contextuels précisés suggèrent l'ajout des dimensions sociales et politiques dans la définition du concept de compétence culturelle. Les deux principales contributions théoriques de cette étude soulignent que l'interaction entre l'infirmière et l'environnement de même que l'expérience clinique sont constitutifs du développement de cette compétence. Les retombées de cette recherche se situent non seulement en formation, mais aussi dans la pratique, la gestion et la recherche en sciences infirmières.

L’expérience d’acquisition de ressources en enseignement à la clientèle et de leur utilisation en situation clinique telle que perçue par des infirmiers ayant suivi un cours e-learning : étude qualitative descriptive

Les infirmiers doivent maintenir leurs connaissances à jour et poursuivre le développement de leurs compétences. Toutefois, dans le contexte actuel de pénurie d’infirmiers, la formation continue représente un défi pour eux. Or, le e-learning semble offrir un potentiel intéressant pour relever ce défi. Une étude qualitative basée sur la méthode des incidents critiques visait à décrire l’expérience clinique d’infirmiers (n=4) suite à un cours e-learning sur l’enseignement à la clientèle. Ce cours de 45 heures était basé sur l’approche par compétences. Des entrevues individuelles ont permis de documenter l’acquisition et l’utilisation en contexte clinique d’apprentissages effectués durant le cours. Les résultats révèlent que ce cours e-learning a permis aux infirmiers qui ont participé à l’étude (n=4) d’acquérir des ressources (connaissances et habiletés) et de les utiliser dans des situations cliniques d’enseignement à la clientèle. Les stratégies pédagogiques, qui apparaissent les plus prometteuses, à la lumière des résultats, sont la discussion de situations cliniques sur le forum de discussion « en ligne » et le projet de mise en contexte réel. En somme, le e-learning, basé sur l’approche par compétences se révèle une approche pédagogique prometteuse pour soutenir le développement des compétences des infirmiers. Mots clés : e-learning, formation continue, stratégies pédagogiques, approche par compétences

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.

Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks

Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework for compactly representing probabilistic information in a variety of unsupervised and supervised learning problems. Often the parameters used in these networks need to be learned from examples. Unfortunately, estimating the parameters via exact probabilistic calculations (i.e, the EM-algorithm) is intractable even for networks with fairly small numbers of hidden units. We propose to avoid the infeasibility of the E step by bounding likelihoods instead of computing them exactly. We introduce extended and complementary representations for these networks and show that the estimation of the network parameters can be made fast (reduced to quadratic optimization) by performing the estimation in either of the alternative domains. The complementary networks can be used for continuous density estimation as well.

The Personal tutorial guidance sessions in the framework of the teaching-learning field of the Bologna process: University of Minho (first cycle) courses students’ perceptions

The Bologna Process defends the adoption of a higher education in teaching-learning methodologies that – in contraposition to the previous model based on the transmission of knowledge, which for being essentially theoretical, gives the student a passive role in the knowledge construction process – allows a (pro) active, autonomous and practical learning, where the student acquires and develops his competences. The personal tutorial guidance sessions are included in the teaching contact hours. This abstract presents a study about the University of Minho (first cycle) Courses Students’ perceptions of the personal tutorial guidance sessions’ relevance in the scope of the learning-teaching process, so as to confirm if the implementation/implantation of the commonly called tutorial (type) education, as an approach to an active, autonomous and practical learning, is sensed by the learners themselves

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Lecture on VLEs and MLEs

Powerpoint Lecture notes on Virtual Learning Environments and Managed Learning Environements

Educating an iPod generation: undergraduate attitudes, experiences and understanding of vodcast and podcast use

ognyline) There is an increasing pressure on university staff to provide ever more information and resources to students. This study investigated student opinions on (audio) podcasts and (video) vodcasts and how well they met requirements and aided learning processes. Two experiments within the Aston University looked at student opinion on, and usage of, podcasts and vodcasts for a selection of their psychology lectures.

Actitudes de los directivos en torno a la adopción del e-learning como herramienta de trabajo en las empresas

El desarrollo del presente documento constituye una investigación sobre las actitudes de los directivos frente a la adopción del e-learning como herramienta de trabajo en las organizaciones de Bogotá. Para ello se realizó una encuesta a 101 directivos, tomando como base el tipo de muestreo de conveniencia; esto con el objetivo de identificar sus actitudes frente al uso del e-learning y su influencia dentro de la organización. Como resultado se obtuvo que las actitudes de los directivos influencian en el uso de herramientas e-learning, así como también en las acciones que promueven su uso y en las actitudes de los empleados; por otro lado se identificó que las creencias relacionadas con la apropiación de herramientas e-learning y los factores facilitadores del uso de estas, influencian en las actitudes de los directivos. Lo anterior, corresponde a los análisis llevados a cabo a partir de los resultados contrastados con los estudios empíricos hallados y el marco teórico desarrollado.

Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.