Embodying feminist research: learning from action research, political practices, diffractions and collective knowledge.

In the past three decades, feminists and critical theorists have discussed and argued the importance of deconstructing and problematizing social science research methodology in order to question normalized hierarchies concerning the production of knowledge and the status of truth claims. Nevertheless, often, these ideas have basically remained theoretical propositions not embodied in research practices. In fact there is very little published discussion about the difficulties and limits of their practical application. In this paper we introduce some interconnected reflections starting from two different but related experiences of embodying 'feminist activist research'. Our aim is to emphasise the importance of attending to process, making mistakes and learning during fieldwork, as well as experimenting with personalized forms of analysis, such as the construction of narratives and the story-telling process.

Embodying feminist research: learning from action research, political practices, diffractions and collective knowledge.

In the past three decades, feminists and critical theorists have discussed and argued the importance of deconstructing and problematizing social science research methodology in order to question normalized hierarchies concerning the production of knowledge and the status of truth claims. Nevertheless, often, these ideas have basically remained theoretical propositions not embodied in research practices. In fact there is very little published discussion about the difficulties and limits of their practical application. In this paper we introduce some interconnected reflections starting from two different but related experiences of embodying 'feminist activist research'. Our aim is to emphasise the importance of attending to process, making mistakes and learning during fieldwork, as well as experimenting with personalized forms of analysis, such as the construction of narratives and the story-telling process.

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.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Reinforcement learning utilizes proxemics: an avatar learns to manipulate the position of people in immersive virtual reality

A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

Information System ProcessInnovation Adoption, Adaptation, Learning, and Unlearning: A Longitudinal Case Study

This thesis examines the history and evolution of information system process innovation (ISPI) processes (adoption, adaptation, and unlearning) within the information system development (ISD) work in an internal information system (IS) department and in two IS software house organisations in Finland over a 43-year time-period. The study offers insights into influential actors and their dependencies in deciding over ISPIs. The research usesa qualitative research approach, and the research methodology involves the description of the ISPI processes, how the actors searched for ISPIs, and how the relationships between the actors changed over time. The existing theories were evaluated using the conceptual models of the ISPI processes based on the innovationliterature in the IS area. The main focus of the study was to observe changes in the main ISPI processes over time. The main contribution of the thesis is a new theory. The term theory should be understood as 1) a new conceptual framework of the ISPI processes, 2) new ISPI concepts and categories, and the relationships between the ISPI concepts inside the ISPI processes. The study gives a comprehensive and systematic study on the history and evolution of the ISPI processes; reveals the factors that affected ISPI adoption; studies ISPI knowledge acquisition, information transfer, and adaptation mechanisms; and reveals the mechanismsaffecting ISPI unlearning; changes in the ISPI processes; and diverse actors involved in the processes. The results show that both the internal IS department and the two IS software houses sought opportunities to improve their technical skills and career paths and this created an innovative culture. When new technology generations come to the market the platform systems need to be renewed, and therefore the organisations invest in ISPIs in cycles. The extent of internal learning and experiments was higher than the external knowledge acquisition. Until the outsourcing event (1984) the decision-making was centralised and the internalIS department was very influential over ISPIs. After outsourcing, decision-making became distributed between the two IS software houses, the IS client, and itsinternal IT department. The IS client wanted to assure that information systemswould serve the business of the company and thus wanted to co-operate closely with the software organisations.

Teaching fundamentals of computing theory: a constructivist approach

A Fundamentals of Computing Theory course involves different topics that are core to the Computer Science curricula and whose level of abstraction makes them difficult both to teach and to learn. Such difficulty stems from the complexity of the abstract notions involved and the required mathematical background. Surveys conducted among our students showed that many of them were applying some theoretical concepts mechanically rather than developing significant learning. This paper shows a number of didactic strategies that we introduced in the Fundamentals of Computing Theory curricula to cope with the above problem. The proposed strategies were based on a stronger use of technology and a constructivist approach. The final goal was to promote more significant learning of the course topics.

A model for the evolution of reinforcement learning in fluctuating games

Many species are able to learn to associate behaviours with rewards as this gives fitness advantages in changing environments. Social interactions between population members may, however, require more cognitive abilities than simple trial-and-error learning, in particular the capacity to make accurate hypotheses about the material payoff consequences of alternative action combinations. It is unclear in this context whether natural selection necessarily favours individuals to use information about payoffs associated with nontried actions (hypothetical payoffs), as opposed to simple reinforcement of realized payoff. Here, we develop an evolutionary model in which individuals are genetically determined to use either trial-and-error learning or learning based on hypothetical reinforcements, and ask what is the evolutionarily stable learning rule under pairwise symmetric two-action stochastic repeated games played over the individual's lifetime. We analyse through stochastic approximation theory and simulations the learning dynamics on the behavioural timescale, and derive conditions where trial-and-error learning outcompetes hypothetical reinforcement learning on the evolutionary timescale. This occurs in particular under repeated cooperative interactions with the same partner. By contrast, we find that hypothetical reinforcement learners tend to be favoured under random interactions, but stable polymorphisms can also obtain where trial-and-error learners are maintained at a low frequency. We conclude that specific game structures can select for trial-and-error learning even in the absence of costs of cognition, which illustrates that cost-free increased cognition can be counterselected under social interactions.

The motivation to learn as a self-presentation tool among Swiss high school students : The moderating role of mastery goals' perceived social value on learning

Although it has been assumed that the motivation to learn - or mastery goal endorsement - positively predicts learning achievement, most empirical findings fail to demonstrate this relationship. In the present research, conducted in a Swiss high school, we adopted a social value approach to test the hypothesis that adolescent students' mastery goals do in fact predict learning, but only if these goals are perceived as highly useful for scholarly success (high social utility), and are not endorsed as a means to be appreciated by the teachers (low social desirability), a finding that has previously been observed among college students and on teacher-graded achievement measures only. Results demonstrate that in spite of potential peculiarities of an adolescent population, individual differences in mastery goals' perceived social utility and desirability moderate the mastery goal endorsement-learning achievement relation. Findings are discussed with regard to both theory development and educational practice.

Online Learning and Teaching in Higher Education, a book review

Peer-reviewed

The Transformation of Practical Teaching Focusing on Dialogic Learning

This study investigates the transformation of practical teaching in a Catalan school, connected to the design, implementation and development of project-based learning, and focusing on dialogic learning to investigate its limits and possibilities. Qualitative and design-based research (DBR) methods are applied. These methods are based on empirical educational research with the theory-driven of learning environments. DBR is proposed and applied using practical guidance for the teachers of the school. It can be associated with the current proposals for Embedding Social Sciences and Humanities in the Horizon 2020 Societal Challenges. This position statement defends the social sciences and the humanities as the most fundamental and important ideas to face all societal challenges. The results of this study show that before the training process, teachers apply dialogic learning in specific moments (for example, when they speak about the weekend); however, during the process and after the process, they work systematically with dialogic learning through the PEPT: they start and finish every activity with a individual and group reflection about their own processes, favouring motivation, reasoning and the implication of all the participants. These results prove that progressive transformations of teaching practice benefit cooperative work in class

Socio-Cultural Transformation and the Promotion of Learning

The main premise of Vygotsky’s cultural-historical theory is that to promotelearning, and thus development, educators must intervene in, and change, the students’ socio-cultural context. Vygotsky’s theory, however, has been misinterpreted and the opposite approach has been accepted: the teaching is adapted, according to the context. The result is widespread failure in schools. This article reclaims the true transformative meaning of Vygotskian theory and shows how successful schools in several countries implement various actions to transform their social and cultural environment. Data is presented from six casestudies of successful schools conducted in five European countries. The analysis showsthat these actions improve instrumental learning and, consequently, cognitive development. All these efforts focus on teaching methods that aim to increase the amount that students learn

Knowledge Sharing in a Virtual Learning Community

The focus of this Master’s Thesis is on knowledge sharing in a virtual Learning community. The theoretical part of this study aims at presenting the theory of knowledge sharing, competence development and learning in virtual teams. The features of successful learning organizations as well as enablers of effective knowledge sharing in virtual communities are also introduced to the reader in the theoretical framework. The empirical research for this study was realized in a global ICT company, specifically in its Human Resources business unit. The research consisted of two rounds of online questionnaires, which were conducted among all the members of the virtual Learning community. The research aim was to find shared opinions concerning the features of a successful virtual Learning community. The analysis of the data in this study was conducted using a qualitative research methodology. The empirical research showed that the main important features of a successful virtual Learning community are members’ passion towards the community way of working as well as the relevance of the content in the virtual community. In general, it was found that knowledge sharing and competence development are important matters in dynamic organizations as well as virtual communities as method and tool for sharing knowledge and hence increasing both individual and organizational knowledge. This is proved by theoretical and by empirical research in this study.

La incidencia de la toma de apuntes sobre el aprendizaje significativo. Un estudio de enseñanza superior.= The incidence of notetaking on meaningful learning. A study of higher education.

En este estudio presentamos una experiencia llevada a cabo con estudiantes de la asignatura “Psicología de la Educación” de diferentes centros universitarios. Tomando como marco de referencia las teorías constructivistas del aprendizaje, el objetivo de nuestro trabajo se centra en comprobar la incidencia de la utilización de diferentes estrategias de enseñanza por parte del profesor y de determinadas estrategias de aprendizaje en el proceso de registrar la información por parte de los estudiantes, en la significatividad del aprendizaje.Los resultados obtenidos muestran que en los grupos donde los profesores han utilizado estrategias de enseñanza diferentes a la clase magistral, se ha producido un cambio positivo en las respuestas de los estudiantes o se ha mantenido el mismo nivel, mientras que el grupo donde se ha utilizado una metodología magistral, el nivel de respuesta es inferior. Así mismo, hemos podido observar como los grupos de estudiantes que utilizan las estrategias de aprendizaje seleccionadas para tomar apuntes mejoran su nivel de respuestas, lo cual no se produce en el grupo control