Effects of Proactive and Retroactive Augmented Information on Physiological Responses in Learning a Novel Motor Skill

Previous research has demonstrated superior learning by participants presented with augmented task information retroactively versus proactively (Patterson & Lee, 2008; 2010). Theoretical explanations of these findings are related to the cognitive effort invested by participants during motor skill acquisition. The present study extended previous research by utilizing the physiological index, power spectral analysis of heart rate variability, previously shown to be sensitive to the degree of cognitive effort invested during the performance of a motor task (e.g., increase cognitive effort results in increased LF/HF ratio). Participants were required to learn 18 different key-pressing sequences. As expected, the proactive condition demonstrated superior RS during acquisition, with the retroactive condition demonstrating superior RS during retention. Measures of LF/HF ratio indicated the retroactive participants were investing significantly less cognitive effort in the retention period compared to the proactive participants (p< .05) as a function of learning.

Breaking the silence : exploring the workplace experiences of six women with learning disabilities

In the current economic climate, employees are expected to upgrade their skills in order to remain productive and competitive in the workplace, and many women with learning disabilities! may feel doubly challenged when dealing with such expectations. Although the number of people with reported learning disabilities who enter the workforce is expected to increase, a dearth of research focuses on work-related experiences of women with learning disabilities; consequently, employers and educators often are unaware ofthe obstacles and demands facing such individuals. This qualitative narrative study sheds light on the work experiences of women with diagnosed or suspected learning disabilities. The study used semistructured interviews to explore their perspectives and reflections on learnlng in order to: (a) raise awareness of the needs of women with learning disabilities, (b) enhance their opportunities to learn in the workplace, and (c) draw attention to the need for improvement of inclusiveness in the workplace, especially for hidden disabilities. Study findings reveal that participants' learning was influenced by work relationships, the learning environments, self-determination, and taking personal responsibility. Moreover, the main accommodation requested was to have supportive and understanding work relationships and environments. Recommendations are made for future research and workplace improvements, most notably that no employees should be left behind through an employee-centered approach.

Kindergarten classroom engagement skills : the road to academic success in elementary school

Les caractéristiques de l’enfant à la maternelle prédisent le succès des transitions à travers les premières années scolaires ainsi que la poursuite académique à l’âge de 22 ans. Les habiletés en mathématiques et langagières à la maternelle sont étroitement liées au rendement scolaire. Cependant, il est également important de tenir compte du rôle de l’autocontrôle et de la maîtrise de soi dans la réussite académique. Spécifiquement, la capacité de suivre des instructions et travailler de manière autonome pourrait faciliter l’adaptation des enfants en milieu scolaire. La présente thèse examine la valeur potentielle de cibler l’engagement scolaire à la maternelle, sous forme d’orientation vers la tâche, pour améliorer l’ajustement académique des enfants au cours du primaire. Une première étude, a examiné si l’engagement scolaire à la maternelle est associé à un meilleur niveau de réussite scolaire et d’ajustement psychosocial à la quatrième année du primaire. Les résultats suggèrent que les habitudes de travail dès l’entrée à l’école représentent des prédicteurs robustes du rendement académique quatre ans plus tard. Un plus haut niveau d’engagement prédit également moins de comportements externalisés et de victimisation par les pairs en quatrième année. Ces résultats sont demeurés significatifs suite au contrôle statistique des habilités en mathématique, langagières et socio-émotionnelles des enfants ainsi que de facteurs de risques familiaux. Une deuxième étude a examiné l’origine de l’engagement scolaire au primaire. Cette étude a permis d’observer que le niveau de contrôle cognitif des enfants d’âge préscolaire représente un prédicteur significatif de l’engagement scolaire à la maternelle. Ces résultats suggèrent l’existence d’une continuité développementale du contrôle cognitif de la petite enfance à la maternelle, et que celle-ci pourrait servir de base pour le développement de bonnes habitudes de travail au primaire. Finalement dans une troisième étude, des analyses centrées sur la personne ont été effectués. Trois sous-groupes d’enfants ont été identifiés dans notre échantillon. Les résultats obtenus indiquent des trajectoires d’engagement bas, moyen et élevé respectivement, au primaire. Le faible contrôle cognitif et les facteurs de risques familiaux ont prédit l’appartenance à la trajectoire d’engagement faible. Dans l’ensemble, les résultats de ces trois études soulignent l’importance de tenir compte de l’engagement dans les évaluations de la maturité scolaire à la maternelle. Cette recherche pourrait également informer le développement de programmes d’interventions préscolaires visant à augmenter la préparation scolaire ainsi que la réduction des écarts au niveau de la réussite académique des enfants.

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.

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.

The Informational Complexity of Learning from Examples

This thesis attempts to quantify the amount of information needed to learn certain tasks. The tasks chosen vary from learning functions in a Sobolev space using radial basis function networks to learning grammars in the principles and parameters framework of modern linguistic theory. These problems are analyzed from the perspective of computational learning theory and certain unifying perspectives emerge.

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.

Online Learning of Non-stationary Sequences

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate a class of universal learning algorithms for this problem by expressing them as simple Bayesian algorithms operating on models analogous to Hidden Markov Models (HMMs). We derive a new performance bound for such algorithms which is considerably simpler than existing bounds. The bound provides the basis for learning the rate at which the identity of the optimal expert switches over time. We find an analytic expression for the a priori resolution at which we need to learn the rate parameter. We extend our scalar switching-rate result to models of the switching-rate that are governed by a matrix of parameters, i.e. arbitrary homogeneous HMMs. We apply and examine our algorithm in the context of the problem of energy management in wireless networks. We analyze the new results in the framework of Information Theory.

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Learning Commonsense Categorical Knowledge in a Thread Memory System

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

Learning Three-Dimensional Shape Models for Sketch Recognition

Artifacts made by humans, such as items of furniture and houses, exhibit an enormous amount of variability in shape. In this paper, we concentrate on models of the shapes of objects that are made up of fixed collections of sub-parts whose dimensions and spatial arrangement exhibit variation. Our goals are: to learn these models from data and to use them for recognition. Our emphasis is on learning and recognition from three-dimensional data, to test the basic shape-modeling methodology. In this paper we also demonstrate how to use models learned in three dimensions for recognition of two-dimensional sketches of objects.

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

Curriculum sequencing for an e-learning system based on learning styles

This work shows the use of adaptation techniques involved in an e-learning system that considers students' learning styles and students' knowledge states. The mentioned e-learning system is built on a multiagent framework designed to examine opportunities to improve the teaching and to motivate the students to learn what they want in a user-friendly and assisted environment

Aula de música : desarrollo de contenidos en una herramienta de e-learning para la asignatura de música en Educación Primaria.

Aula de música es una herramienta e-learning para el desarrollo del aprendizaje de la música para niños con edades comprendidas entre los 6 y 12 años, edades correspondientes a las de los alumnos de la etapa de la Educación Primaria. En esta herramienta destaca el uso de estándares y especificaciones como LOM, IMS, etc. que van a facilitar la tarea de reutilizar la documentación incluida para compartir conocimiento. El proceso de elaboración del contenido ha sido fundamental y en relación con el entorno de trabajo debe mencionarse que se ha primado la construcción de una GUI que sirva para aprender y que motive a los alumnos a aprender música de una forma diferente, en contraposición a realizar una diseño estético que fuera incapaz de adaptarse a las capacidades de cada tipo de usuario, para lo que se han tenido en cuenta criterios de usabilidad y accesibilidad (WAI).