Anxiety and learning / response behaviour in the context of intellectual problem solving by young children

Twenty-eight grade four students were ca.tegorized as either high or low anxious subjects as per Gillis' Child Anxiety Scale (a self-report general measure). In determining impulsivity in their response tendencies, via Kagan's Ma.tching Familiar Figures Test, a significant difference between the two groups was not found to exist. Training procedures (verbal labelling plus rehearsal strategies) were introduced in modification of their learning behaviour on a visual sequential memory task. Significantly more reflective memory recall behaviour was noted by both groups as a result. Furthermore, transfer of the reflective quality of this learning strategy produced significantly less impulsive response behaviour for high and low anxious subjects with respect to response latency and for low anxious subjects with respect to response accuracy.

Conceptualizing Institutional Capacity Building Through a Learning Process Set in Motion by a Foundation

Employing critical pedagogy and transformative theory as a theoretical framework, I examined a learning process associated with building capacity in community-based organizations (CBOs) through an investigation of the Institutional Capacity Building Program (ICBP) initiated by a Foundation. The study sought to: (a) examine the importance of institutional capacity building for individual and community development; (b) investigate elements of a process associated with a program and characteristics of a learning process for building capacity in CBOs; and (c) analyze the Foundation’s approach to synthesizing, systematizing, and sharing learning. The study used a narrative research design that included 3 one-on-one, hour-long interviews with 2 women having unique vantage points in ICBP: one is a program facilitator working at the Foundation and the other runs a CBO supported by the Foundation. The interviews’ semistructured questions allowed interviewees to share stories regarding their experience with the learning process of ICB and enabled themes to emerge from their day-to-day experience. Through the analysis of this learning process for institutional capacity building, a few lessons can be drawn from the experience of the Foundation.

Learning analysis by reduction from positive data

Analysis by reduction is a linguistically motivated method for checking correctness of a sentence. It can be modelled by restarting automata. In this paper we propose a method for learning restarting automata which are strictly locally testable (SLT-R-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by SLT-R-automata with respect to the Chomsky hierarchy.

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.

People Recognition in Image Sequences by Supervised Learning

We describe a system that learns from examples to recognize people in images taken indoors. Images of people are represented by color-based and shape-based features. Recognition is carried out through combinations of Support Vector Machine classifiers (SVMs). Different types of multiclass strategies based on SVMs are explored and compared to k-Nearest Neighbors classifiers (kNNs). The system works in real time and shows high performance rates for people recognition throughout one day.

Investigating the use of Virtual Learning Environments by teachers in schools and colleges

Investigating the use of Virtual Learning Environments by teachers in schools and colleges

Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.

Amazing science experiments : 150 easy projects using everyday materials, demostrated step by step in 1300 photographs.

Colección de ciento cincuenta experimentos que explican hechos y procesos científicos, y demuestran cómo funcionan las máquinas. Cada experimento se introduce, describe y explica, y esta información no sólo permite al usuario trabajar con confianza, sino que también proporciona una gran cantidad de conocimiento científico adicional para los niños de ocho a doce años. Dividido en cuatro secciones, cada una explora con un tema cotidiano diversas áreas especializadas de la ciencia: nuestra tierra inquieta, maravillas naturales cerca de casa, maravillas físicas y materiales, viajes y transporte.

How to define more sustainable M-learning projects for development

Resumen basado en el de la publicaci??n

Educational projects based on mobile learning

Resumen basado en el de la publicaci??n

Providing pervasive learning eXperiences by combining Internet of Things and e-Learning standard

Resumen basado en el de la publicaci??n

Language learning strategy use by Spanish EFL students : the effect of proficiency level, gender, and motivation

Resumen basado en el de la publicaci??n

Social Networking Sites as Technology Enhanced Learning Communities towards autonomous language learning

The following contribution pretends to cope with the demands of a globalised, post-modern environment through the design and implementation of an online international project where an SNS is used in order to join English as Second Language (ESL) students from different parts of the world. The design of the project appears around the implementation of the Bologna process in the Faculty of Education from the University of Girona where the basic prerequisite of all students to acquire English at the level B1 of the Common European Portfolio makes English a compulsory competence for communication among its higher education candidates in order to develop in the world. Together with the University of Girona, there is the International Educational and Resources Network (iEARN) which promotes the participation of schools around the world in online international projects

The formation of learning sets by the chinchilla in an auditory discrimination procedure

This paper discusses a study conducted to test sound discrimination abilities of the chinchilla.