Impact on assertivity of a social skills training with adolescents suffering from psychotic or anxiety/mood disorders: An exploratory study

During adolescence numerous of important social abilities are acquired within interactions with peers. Severe psychiatric disorders interfere with the acquisition of these social skills. For example, due to excessive shyness, adolescents with psychiatric disorders may not experiment positive social interactions. Social skills training (SKT) may help adolescents to remediate to these diffi culties. This exploratory study aims to assess the SKT's effect on assertivity, in a population of adolescents presenting psychiatric disorder and attending a day care unit for adolescents. The SKT, delivered in group, deals with different themes such as contact, conversation, problem solving, confl ict, fail, success, learning, effort, separation, breakdown, and project. In this context, 38 adolescents (19 suffering from anxiety / mood disorder and 19 suffering from psychotic disorder) rate their level of assertivity before and after a SKT with the Rathus assertivity scale. This scale allows to differentiate between inhibited, assertive and assertiveaggressive adolescents. Results showed a general improvement on assertivity after the SKT. More specifi cally, adolescents suffering from anxiety disorder and the 'inhibited' adolescents showed the higher benefi t from the SKT. Thus, two hours per week of SKT seems to enhance social abilities in a population with severe psychiatric disorders. More specifi - cally, adolescents with anxiety / mood disorders reported more benefi ts of the SKT on their assertivity. Nevertheless, adolescents with psychotic disorders did not report strong benefi ts from the SKT despite the improvement observed at a clinical level. This observation raises questions about the usefulness of self-reported questionnaire to measure such benefi t for adolescents with psychosis.

Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

The learning about imaging diagnosis technology

Objective To investigate the learning about management of the technology (efficient use, acquisition and maintenance of imaging diagnosis equipment) in the radiology residency program of Escola Paulista de Medicina - Universidade Federal de São Paulo, with a view to improving the education of radiologists. Materials and Methods Exploratory research where residents, faculty staff and tutors of the program were quantitative and qualitatively approached with Likert scale questionnaires (46), and deepening with recorded interviews (18) and categorization based upon meaning units (thematic analysis). Results Among the participants, 66% agreed that they had the opportunity of learning about the use of radiological equipment; for 61% the program should include knowledge on the importance of acquiring equipment; and 72% emphasized the lack of learning about equipment management and maintenance. Conclusion As the major moment in the education of specialists, the medical residency program provides residents with a favorable environment to the learning of the skills required to the future of their professional practice, but with limited emphasis on the management of the technology: efficient use, acquisition and mainly maintenance of equipment, still poorly explored. Both the investigated program and the medical residency in radiology should incorporate, whenever possible, the commitment with the training in supplementary skills related to equipment management, developing the competence of the future radiologists.

Learning on human resources management in the radiology residency program

Objective To investigate the process of learning on human resource management in the radiology residency program at Escola Paulista de Medicina – Universidade Federal de São Paulo, aiming at improving radiologists' education. Materials and Methods Exploratory study with a quantitative and qualitative approach developed with the faculty staff, preceptors and residents of the program, utilizing a Likert questionnaire (46), taped interviews (18), and categorization based on thematic analysis. Results According to 71% of the participants, residents have clarity about their role in the development of their activities, and 48% said that residents have no opportunity to learn how to manage their work in a multidisciplinary team. Conclusion Isolation at medical records room, little interactivity between sectors with diversified and fixed activities, absence of a previous culture and lack of a training program on human resources management may interfere in the development of skills for the residents' practice. There is a need to review objectives of the medical residency in the field of radiology, incorporating, whenever possible, the commitment to the training of skills related to human resources management thus widening the scope of abilities of the future radiologists.

Reflective Learning in Higher Education: A Comparative Analysis

A descriptive, exploratory study is presented based on a questionnaire regarding the following aspects of reflective learning: a) self-knowledge, b) relating experience to knowledge, c) self-reflection, and d) self-regulation of the learning processes. The questionnaire was completed by students studying four different degree courses (social education, environmental sciences, nursing, and psychology). Specifically, the objectives of a self-reported reflective learning questionnaire are: i) to determine students’ appraisal of reflective learning methodology with regard to their reflective learning processes, ii) to obtain evidence of the main difficulties encountered by students in integrating reflective learning methodologies into their reflective learning processes, and iii) to collect students’ perceptions regarding the main contributions of the reflective learning processes they have experienced

Desired learning outcomes for the curricula in financial services industry as perceived by professionals in the financial services industry located in the Greater Toronto Area /

This study sought to explore the changing nature of the financial services industry in Toronto, Canada and the impact that these changes will have on the vocational educational outcomes required by Ontario Colleges of Applied Arts and Technology (CAAT) graduates who wish to enter the financial services industry. The study was descriptive and exploratory, based on both quantitative and qualitative data. Triangulation of 3 data sources (a collection of newspaper articles from the Toronto Star between July 1999 and June 2000, the calendars of the 25 CAATs, and a survey questionnaire prepared by me and distributed to subject matter experts who are key practitioners in the financial services industry) was used. The study contains a discussion of how the financial services industry is changing. The first question to be answered was: What do current practitioners in financial services perceive to be the knowledge, skills, and attitudes that will be required of future graduates for employment within the financial services industry? The study found that Ontario CAAT's graduates entering the financial services field need both business and financial services vocational learning outcomes. Colleges should have 2 programs 1 in accounting and 1 in financial services. The report addresses which specific topics should be included in the financial services program. The second question to be answered was: How does this anticipated profile of knowledge, skills, and attitudes change depending on the degree of implementation of the new technologies by the survey respondent? The study found no pattern. The third question to be answered was: In what way do existing programs need to change in the area of accreditation as perceived by the respondents? The study found that for accreditation, 3 credentials should be addressed within the financial services program. These are the Canadian Securities, the Life Underwriters, and the Certified Financial Planner designations. The last question to be answered was: What new knowledge, skills, and attitudes need to be incorporated into college curricula to address changing needs in the employment sector? For each Ontario CAAT which has a financial services program (excluding accounting), their program was reviewed in light of the topics as perceived by professionals in the financial services industry.

Exploring the events that engage graduate students in transformative learning /

This research explored the events that engaged graduate students in transformative learning within a graduate program in education. This context was chosen because one objective of a graduate program is to facilitate critical thinking and transformative learning. The question ofhow adult learners perceive and experience learning steered the direction ofthis study. However, the purpose ofthis research was to study critical incidents that led to profound cognitive and affective changes as perceived by the graduate students. Specifically, the questions to be answered were what critical incidents happened to graduate students while in the Master ofEducation program, how were the incidents experienced, and what transformation resulted? The research design evolved over the course of a year and was highly influenced by previous empirical studies and criticisms oftransformative learning theory. The overall design was qualitative and phenomenological. A critical and interpretive approach was made to empirical data collected through a critical incident questionnaire and in-depth interviews. Inductive analysis allowed theory to be built from the data by making comparisons. New questions emerged and attention was given to social context, the passage oftime, and sequence ofevents in order to give meaning and translation ofthe participants' experiences and to build the interpretive narratives. Deductive analysis was also used on the data and a blending ofthe two forms of analysis; this resulted in the development ofa foundational model for transformative learning to be built.The data revealed critical incidents outside ofthe graduate school program that occurred in childhood or adult life prior to graduate school. Since context of individuals' lives had been an important critique of past transformative learning models and studies, this research expanded the original boundaries of this study beyond graduate school to incorporate incidents that occurred outside of graduate school. Critical incidents were categorized into time-related, people-related, and circumstancerelated themes. It was clear that participants were influenced and molded by the stage oftheir life, personal experiences, familial and cultural conditioning, and even historic events. The model developed in this document fiom an overview ofthe fmdings identifies a four-stage process of life difficulty, disintegration, reintegration, and completion that all participants' followed. The blended analysis was revealed from the description ofhow the incidents were experienced by the participants. The final categories were what were the feelings, what was happening, and what was the enviromnent? The resulting transformation was initially only going to consider cognitive and affective changes, however, it was apparent that contextual changes also occurred for all participants, so this category was also included. The model was described with the construction metaphor of a building "foimdation" to illustrate the variety of conditions that are necessary for transformative learning to occur. Since this was an exploratory study, no prior models or processes were used in data analysis, however, it appeared that the model developed from this study incorporated existing models and provided a more encompassing life picture oftransformative learning.

Perceived learning needs in staff development of some care providers in five long-term care settings in Southern Ontario /

This exploratory descriptive study described what 20 care providers in 5 long-term care facilities perceived to aid or hinder their learning in a work-sponsored learning experience. A Critical Incident Technique (Woolsey, 1986) was the catalyst for the interviews with the culturally and professionally diverse participants. Through data analysis, as described by Moustakas (1994), I found that (a) humour, (b) the learning environment, (c) specific characteristics of the presenter such as moderate pacing, speaking slowly and with simple words, (d) decision-making authority, (e) relevance to practice, and (f) practical applications best met the study participants' learning needs. Conversely, other factors could hinder learning based on the participants' perceptions. These were: (a) other presenter characteristics such as a program that was delivered quickly or spoken at a level above the participants' comprehension, (b) no perceived relevance to practice, (c), other environmental situations, and (d) the timing of the learning session. One of my intentions was to identify the emic view among cultural groups and professional/vocational affiliations. A surprising finding of this study was that neither impacted noticeably on the perceived learning needs of the participants. Further research with a revised research design to facilitate inclusion of more diverse participants will aid in determining if the lack of a difference was unique to this sample or more generalizable on a case-to-case transfer basis to the study population.

Continuing education learning preferences and styles of legal clinic lawyers /

The purpose of this qualitative research was to study the learning preferences and styles of management lawyers who work in Ontario's legal aid clinics. Data were gathered from two sources and analyzed using the constant comparison method. A preand postconference survey provided the principal data on clinic lawyers' learning preferences. Follow-up interviews were then conducted with 3 purposefully selected survey participants to explore their personal learning styles. Kolb's experiential learning theory provided the theoretical framework for discussing personal learning styles. The findings showed a general consistency among the lawyers to learn by listening to lectures and experts. This preference may suggest a lingering influence from law school training. The lawyers' more informal learning associated with daily practice, however, appeared to be guided by various learning styles. The learning style discussions provided some support for Kolb's model but also confirmed some shortcomings noted by other authors. Educators who design continuing education programs for lawyers may benefit from some insights gained from this exploratory research. This study adds to a limited but growing body of work on the learning preferences and styles of lawyers and suggests new questions for future research.

Econometrics as an Academic Discipline in Higher Education: An Exploratory Study of the Need, Relevance and Significance

Econometrics is a young science. It developed during the twentieth century in the mid-1930’s, primarily after the World War II. Econometrics is the unification of statistical analysis, economic theory and mathematics. The history of econometrics can be traced to the use of statistical and mathematics analysis in economics. The most prominent contributions during the initial period can be seen in the works of Tinbergen and Frisch, and also that of Haavelmo in the 1940's through the mid 1950's. Right from the rudimentary application of statistics to economic data, like the use of laws of error through the development of least squares by Legendre, Laplace, and Gauss, the discipline of econometrics has later on witnessed the applied works done by Edge worth and Mitchell. A very significant mile stone in its evolution has been the work of Tinbergen, Frisch, and Haavelmo in their development of multiple regression and correlation analysis. They used these techniques to test different economic theories using time series data. In spite of the fact that some predictions based on econometric methodology might have gone wrong, the sound scientific nature of the discipline cannot be ignored by anyone. This is reflected in the economic rationale underlying any econometric model, statistical and mathematical reasoning for the various inferences drawn etc. The relevance of econometrics as an academic discipline assumes high significance in the above context. Because of the inter-disciplinary nature of econometrics (which is a unification of Economics, Statistics and Mathematics), the subject can be taught at all these broad areas, not-withstanding the fact that most often Economics students alone are offered this subject as those of other disciplines might not have adequate Economics background to understand the subject. In fact, even for technical courses (like Engineering), business management courses (like MBA), professional accountancy courses etc. econometrics is quite relevant. More relevant is the case of research students of various social sciences, commerce and management. In the ongoing scenario of globalization and economic deregulation, there is the need to give added thrust to the academic discipline of econometrics in higher education, across various social science streams, commerce, management, professional accountancy etc. Accordingly, the analytical ability of the students can be sharpened and their ability to look into the socio-economic problems with a mathematical approach can be improved, and enabling them to derive scientific inferences and solutions to such problems. The utmost significance of hands-own practical training on the use of computer-based econometric packages, especially at the post-graduate and research levels need to be pointed out here. Mere learning of the econometric methodology or the underlying theories alone would not have much practical utility for the students in their future career, whether in academics, industry, or in practice This paper seeks to trace the historical development of econometrics and study the current status of econometrics as an academic discipline in higher education. Besides, the paper looks into the problems faced by the teachers in teaching econometrics, and those of students in learning the subject including effective application of the methodology in real life situations. Accordingly, the paper offers some meaningful suggestions for effective teaching of econometrics in higher education

Role of the cultural values as a key part in the implementing of healthy practices : exploratory research

El concepto de organización saludable cada vez toma más fuerza en el ámbito empresarial y académico, a razón de su enfoque integral y al impacto generado en distintos grupos de interés. Debido a su reciente consolidación como concepto, existe un limitado cuerpo de investigación en torno al tema. Para contribuir a la generación de conocimiento en este sentido, se desarrolló un estudio exploratorio el cual tenía como objetivo identificar la relación existente entre la implementación de prácticas saludables en las organizaciones y los valores culturales. En el estudio participaron 66 sujetos a quienes se les administró un cuestionario compuesto por nueve variables, cinco provenientes del modelo de Hofstede (1980) y cuatro más que evaluaban la implementación de prácticas organizacionales saludables. Los resultados obtenidos muestran que los valores culturales predicen la implementación de prácticas saludables.

The instrumentality of the portfolios in the teach-learning process: study with higher education Portuguese teachers

In a time when higher education come for deep changes and if intends an education more centered in the pupil, the teach-learning portfolios appears as a tool to use, because versatile and with innumerable potentialities. This article reveals the results gotten with higher education teachers, who we looked for to know if these appeal in use the teach-learning portfolios, in the curricular units that teach. We looked for, equally, to perceive of that forms these are used. This is an exploratory study, basically descriptive, that does not have pretensions to generalize for all the teaching population. We elaborated and we applied a questionnaire, with 290 teachers of higher education public, university and polytechnic. We verify that the percentage of the teachers that uses the portfolios in the teach- learning process is not very raised.