Développement d’une grille pour l’analyse de la culture e-learning dans des forums de discussion en ligne

La formation à distance (FAD) est de plus en plus utilisée dans le cadre de la formation des enseignants aux technologies de l’information et de la communication (TIC). Dans les pays en voie de développement, elle permet non seulement de réduire les coûts par rapport à une formation traditionnelle, mais aussi de modéliser des pratiques pédagogiques exemplaires qui permettent de maximiser le recours aux TIC. En ce sens, la formation continue des enseignants aux TIC par des cours à distance qui intègrent des forums de discussion offre plusieurs avantages pour ces pays. L’évaluation des apprentissages réalisés dans les forums reste cependant un problème complexe. Différents modèles et différentes procédures d’évaluation ont été proposés par la littérature, mais aucun n’a encore abordé spécifiquement la culture e-learning des participants telle qu’elle est définie par le modèle IntersTICES (Viens, 2007 ; Viens et Peraya, 2005). L’objectif de notre recherche est l’élaboration d’une grille opérationnelle pour l’analyse de la culture e-learning à partir des contenus de différents forums de discussion utilisés comme activité de formation dans un cours à distance. Pour développer cette grille, nous utiliserons une combinaison de modèles recensés dans la revue de littérature afin de circonscrire les principaux concepts et indicateurs à prendre en compte pour ensuite suivre les procédures relatives à l’analyse de la valeur, une méthodologie qui appelle la production d’un cahier des charges fonctionnel, la production de l’outil, puis sa mise à l’essai auprès d’experts. Cette procédure nous a permis de mettre sur pied une grille optimale, opérationnelle et appuyée par une base théorique et méthodologique solide.

Cyberjustice in Brazil : the use of technology to enhance access to justice and procedural celerity

Dans une société mondialisée, où les relations sont intégrées à une vitesse différente avec l'utilisation des technologies de l'information et des communications, l'accès à la justice gagne de nouveaux concepts, mais elle est encore confrontée à de vieux obstacles. La crise mondiale de l'accès à la justice dans le système judiciaire provoque des débats concernant l'égalité en vertu de la loi, la capacité des individus, la connaissance des droits, l'aide juridique, les coûts et les délais. Les deux derniers ont été les facteurs les plus importants du mécontentement des individus avec le système judiciaire. La présente étude a pour objet d'analyser l'incidence de l'utilisation de la technologie dans l’appareil judiciaire, avec l'accent sur la réalité brésilienne, la voie législative et des expériences antérieures dans le développement de logiciels de cyberjustice. La mise en œuvre de ces instruments innovants exige des investissements et de la planification, avec une attention particulière sur l'incidence qu'ils peuvent avoir sur les routines traditionnelles des tribunaux. De nouveaux défis sont sur la voie de ce processus de transformation et doivent être traités avec professionnalisme afin d'éviter l'échec de projets de qualité. En outre, si la technologie peut faire partie des différents aspects de notre quotidien et l'utilisation de modes alternatifs de résolution des conflits en ligne sont considérés comme un succès, pourquoi serait-il difficile de faire ce changement dans la prestation de la justice par le système judiciaire? Des solutions technologiques adoptées dans d'autres pays ne sont pas facilement transférables à un environnement culturel différent, mais il y a toujours la possibilité d'apprendre des expériences des autres et d’éviter de mauvaises voies qui pourraient compromettre la définition globale de l'accès à la justice.

Modified Large Margin Nearest Neighbor Metric Learning for Regression

The main objective of this letter is to formulate a new approach of learning a Mahalanobis distance metric for nearest neighbor regression from a training sample set. We propose a modified version of the large margin nearest neighbor metric learning method to deal with regression problems. As an application, the prediction of post-operative trunk 3-D shapes in scoliosis surgery using nearest neighbor regression is described. Accuracy of the proposed method is quantitatively evaluated through experiments on real medical data.

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.

Influence of personality factors on the consumption of personal care products

The study is significant from both an application perspective of marketing management as well as from an academic angle. The market for personal care products is a highly fragmented one, with intense competition for specific niche segments. It is well known in marketing literature that the bulk of the volume of sale is accounted for by the minority who are the heavy users. This study will help the marketers to identify the personality profile of such a group and understand how the interaction of personality factors at least partially explains differences in consumption. This knowledge might be useful for better segmentation using psychographic variables as well as for designing specific advertisement campaigns to target the vulnerable groups of customers. From a theoretical perspective, the research may contribute to understanding how specific personality variables and their interaction lead to differences in consumption. The knowledge corresponding to self theory, social comparison theory, persuasibility, evidence from psychology of eating disorders: these all may be integrated into a common frame work for explaining consumption of products having a social function.

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Sprachentwicklung und Fachlernen im Mechanikunterricht

Die empirische Studie untersucht das Wechselspiel zwischen der fachbezogenen Sprachentwicklung und dem Fachlernen von Schülerinnen und Schülern bei der Einführung in den Kraftbegriff. Sie betrachtet also sowohl sprachliche wie auch kognitive Aspekte des Lernens in der Mechanik. Dafür wurde ein Unterrichtskonzept entwickelt, das den Gebrauch des Fachwortes Kraft in der Wissenschaft und in der alltäglichen Sprache besonders thematisiert. Dieses Unterrichtskonzept basiert auf Empfehlungen und Ergebnissen der Kognitionspsychologie, Linguistik, Philosophie, Sprachlehrforschung und der Didaktiken der Physik und der Fremdsprachen. Im Rahmen des Unterrichts wurden die Schülerinnen und Schüler mit zwei Aufgabentypen konfrontiert: Beim ersten Aufgabentyp waren die Lerner aufgefordert, den Kraftbegriff so zu verwenden, wie es einer fachsprachlich angemessenen Form entspräche, etwa um die Bewegung eines Zuges zu beschreiben. Aufgaben des zweiten Typs sahen vor, dass die Schülerinnen und Schüler kurze Texte danach klassifizierten, ob sie der Alltagssprache oder der Fachsprache angehörten. Diese als Metadiskurs bezeichnete Form der Auseinandersetzung mit sprachlichen Aspekten verhalf den Schülerinnen und Schülern zu einer Gelegenheit, ihr eigenes Verständnis des Kraftbegriffs zu thematisieren. Weiter lieferte der Metadiskurs wichtige Hinweise darauf, ob die Schülerinnen und Schüler sich bei ihren Beurteilungen eher auf formal-sprachliche oder inhaltliche Aspekte der Sprache bezogen. Für die Datenerhebung wurden alle Unterrichtsstunden videografiert und transkribiert. Zusammen mit schriftlichen Arbeitsergebnissen und Tests stand ein umfangreicher Datensatz zur Verfügung, für dessen Auswertung ein inhaltsanalytisches Verfahren Anwendung fand. Die Ergebnisse zeigen, dass das Lernen im Fach Physik bestimmte Ähnlichkeiten mit dem Lernen einer Fremdsprache zeigt: Wenn die Schülerinnen und Schüler den Kraftbegriff fachsprachlich verwenden sollen, sehen sie sich oft einer Alternativentscheidung gegenüber. Entweder sie versuchen, einer fachsprachlichen Form zu gehorchen und verlieren dabei den Inhalt aus den Augen, oder sie konzentrieren sich auf den Inhalt, drücken sich dabei aber in ihrer Alltagssprache aus und folgen Alltagskonzepten, die weit entfernt von den fachlich intendierten liegen. Ähnliche Beobachtungen kann man im Sprachunterricht machen, wenn Schüler eine neue grammatische Regel einüben: Sie konzentrieren sich entweder auf die neu zu erlernende Regel, oder aber auf den Inhalt des Gesagten, wobei sie die grammatische Regel, die an sich Gegenstand der Übung ist, verletzen. Meistens fällt diese Entscheidung derart, dass die Konzentration auf den Inhalt des Gesagten gerichtet ist, nicht oder wenig auf seine Form. Im Unterschied zum Sprachunterricht ist der Physikunterricht allerdings nicht nur darauf gerichtet, fachsprachlich angemessene Formen einzuüben, sondern insbesondere darauf, den Blick für neue und ungewohnte Konzepte zu öffnen. Damit müssen die Schülerinnen und Schüler hier häufig sprachliche und kognitive Hürden zur selben Zeit bewältigen. Die detaillierte Analyse des Metadiskurses zeigt, dass das Problem des Nebeneinanders zweier unterschiedlicher Anforderung entschäft werden kann: Während die Schüler im Metadiskurs unterschiedliche Aspekte der Sprache diskutieren, sind sie eher in der Lage, sowohl formale wie inhaltsbezogene Merkmale der Sprache wahrzunehmen. Der Text referiert weitere Parallelen zwischen dem Physikunterricht und dem Fremdsprachenlernen, sodass die Auffassung gerechtfertigt ist, dass die Fremdsprachendidaktik als Ideenlieferantin dafür dienen kann, neue Verbesserungsmöglichkeiten für den Physikunterricht aufzufinden.

Task-Level Robot Learning

We are investigating how to program robots so that they learn from experience. Our goal is to develop principled methods of learning that can improve a robot's performance of a wide range of dynamic tasks. We have developed task-level learning that successfully improves a robot's performance of two complex tasks, ball-throwing and juggling. With task- level learning, a robot practices a task, monitors its own performance, and uses that experience to adjust its task-level commands. This learning method serves to complement other approaches, such as model calibration, for improving robot performance.

Learning from Ambiguity

There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. Each example is a bag, consisting of any number of instances. A bag is labeled negative if all instances in it are negative. A bag is labeled positive if at least one instance in it is positive. Because the instances themselves are not labeled, each positive bag is an ambiguous example. We would like to learn a concept which will correctly classify unseen bags. We have developed a measure called Diverse Density and algorithms for learning from multiple-instance examples. We have applied these techniques to problems in drug design, stock prediction, and image database retrieval. These serve as examples of how to translate the ambiguity in the application domain into bags, as well as successful examples of applying Diverse Density techniques.

Representation and Detection of Shapes in Images

We present a set of techniques that can be used to represent and detect shapes in images. Our methods revolve around a particular shape representation based on the description of objects using triangulated polygons. This representation is similar to the medial axis transform and has important properties from a computational perspective. The first problem we consider is the detection of non-rigid objects in images using deformable models. We present an efficient algorithm to solve this problem in a wide range of situations, and show examples in both natural and medical images. We also consider the problem of learning an accurate non-rigid shape model for a class of objects from examples. We show how to learn good models while constraining them to the form required by the detection algorithm. Finally, we consider the problem of low-level image segmentation and grouping. We describe a stochastic grammar that generates arbitrary triangulated polygons while capturing Gestalt principles of shape regularity. This grammar is used as a prior model over random shapes in a low level algorithm that detects objects in images.

Learning from Incomplete Data

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.

Guia per a l'adaptació a l'espai europeu d'educació superior: 4. Activitats d'aprenentatge = Guía para la adaptación al espacio europeo de educación superior: 4. Actividades de aprendizaje = Guide to Adaptation to the European Higher Education Area: 4. Learning Activities

Aquest quadern és el quart lliurement de la Guia per a l'adaptació a l'espai europeo superior. Té l'origen en el debat de la Comissió de Seguiment del Pla Pilot d'adaptació a l'Espai Europeu d'Educació Superior de la UdG i del grup de treball que s'ha constituït l'estiu del 2006 expressament per tractar el tema de les activitats d'aprenentatge

Approaches to Learning

Looks at some of the models of learning and discusses how they apply to university students

Discovery-Based Learning - Daniel Rowan

This is one of a series of short case studies describing how academic tutors at the University of Southampton have made use of learning technologies to support their students.

Problem-Based Learning Using CaseMaker - Richard Collier

This is one of a series of short case studies describing how academic tutors at the University of Southampton have made use of learning technologies to support their students.