Reversal of activity-mediated spine dynamics and learning impairment in a mouse model of Fragile X syndrome.

Fragile X syndrome (FXS) is characterized by intellectual disability and autistic traits, and results from the silencing of the FMR1 gene coding for a protein implicated in the regulation of protein synthesis at synapses. The lack of functional Fragile X mental retardation protein has been proposed to result in an excessive signaling of synaptic metabotropic glutamate receptors, leading to alterations of synapse maturation and plasticity. It remains, however, unclear how mechanisms of activity-dependent spine dynamics are affected in Fmr knockout (Fmr1-KO) mice and whether they can be reversed. Here we used a repetitive imaging approach in hippocampal slice cultures to investigate properties of structural plasticity and their modulation by signaling pathways. We found that basal spine turnover was significantly reduced in Fmr1-KO mice, but markedly enhanced by activity. Additionally, activity-mediated spine stabilization was lost in Fmr1-KO mice. Application of the metabotropic glutamate receptor antagonist α-Methyl-4-carboxyphenylglycine (MCPG) enhanced basal turnover, improved spine stability, but failed to reinstate activity-mediated spine stabilization. In contrast, enhancing phosphoinositide-3 kinase (PI3K) signaling, a pathway implicated in various aspects of synaptic plasticity, reversed both basal turnover and activity-mediated spine stabilization. It also restored defective long-term potentiation mechanisms in slices and improved reversal learning in Fmr1-KO mice. These results suggest that modulation of PI3K signaling could contribute to improve the cognitive deficits associated with FXS.

Evolutionarily stable learning schedules and cumulative culture in discrete generation models.

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium.

Social and individual learning of helping in humans and other species.

Helping behaviors can be innate, learned by copying others (cultural transmission) or individually learned de novo. These three possibilities are often entangled in debates on the evolution of helping in humans. Here we discuss their similarities and differences, and argue that evolutionary biologists underestimate the role of individual learning in the expression of helping behaviors in humans.

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.

Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

The Roles of Cultural, Functional and Value Diversity in Innovativeness and Learning

Tutkielman tavoite on tutkia kulttuurista, funktionaalista ja arvojen diversiteettiä, niiden suhdetta innovatiivisuuteen ja oppimiseen sekä tarjota keinoja diversiteetin johtamiseen. Tämän lisäksi selvitetään linjaesimiesten haastattelujen kautta miten diversiteetti case -organisaatiossa tällä hetkellä koetaan. Organisaation diversiteetin tämänhetkisen tilan tunnistamisen kautta voidaan esittää parannusehdotuksia diversiteetin hallintaan. Tutkimus- ja tiedonkeruumenetelmänä käytetään kvalitatiivista focus group haastattelumenetelmää. Tutkimuksessa saatiin selkeä kuva kulttuurisen, funktionaalisen ja arvojen diversiteetin merkityksistä organisaation innovatiivisuudelle ja oppimiselle sekä löydettiin keinoja näiden diversiteetin tyyppien johtamiseen. Tutkimuksen tärkeä löydös on se, että diversiteetti vaikuttaa positiivisesti organisaation innovatiivisuuteen kun sitä johdetaan tehokkaasti ja kun organisaatioympäristö tukee avointa keskustelua ja mielipiteiden jakamista. Case organisaation tämänhetkistä diversiteetin tilaa selvitettäessä havaittiin että ongelma organisaatiossa ei ole diversiteetin puute, vaan paremminkin se, ettei diversiteettia osata hyödyntää. Organisaatio ei tue erilaisten näkemysten ja mielipiteiden vapaata esittämistä jahyväksikäyttöä ja siksi diversiteetin hyödyntäminen on epätäydellistä. Haastatteluissa tärkeinä seikkoina diversiteetin hyödyntämisen parantamisessa nähtiin kulttuurin muuttaminen avoimempaan suuntaan ja johtajien esimiestaitojen parantaminen.

Agile Methods in Small Software Projects

Agile software development methods are attempting to provide an answer to the software development industry's need of lighter weight, more agile processes that offer the possibility to react to changes during the software development process. The objective of this thesis is to analyze and experiment the possibility of using agile methods or practices also in small software projects, even in projects containing only one developer. In the practical part of the thesis a small software project was executed with some agile methods and practices that in the theoretical part of the thesis were found possible to be applied to the project. In the project a Bluetooth proxy application that is run in the S60 smartphone platform and PC was developed further to contain some new features. As a result it was found that certain agile practices can be useful even in the very small projects. The selection of the suitable practices depends on the project and the size of the project team.

M-Learning + Facebook: experiencias y percepciones de estudiantes de diseño

Durante los últimos años, diversas instituciones y universidades han comenzado a experimentar con el m-learning y Facebook a través de diferentes proyectos como parte de sus metodologías de aprendizaje y como una oportunidad para trabajar con los jóvenes. Sin embargo, poco se sabe de las percepciones y experiencias que pueden obtener estudiantes de diseño sobre este tema. En este estudio 24 estudian - tes han completado sus actividades de aprendizaje durante dos meses, utilizando un smarthphone y la popular red social Facebook. Al final del plazo, los estudiantes participaron además en un grupo de discusión para expresar sus experiencias. Los resultados indicaron que los estudiantes utilizaron Facebook como parte de su rutina diaria y que fueron creadores de contenido proporcionando estos a otros. Además los resultados indican que durante el primer mes perdieron mucho tiempo observando contenidos propuestos en Facebook, que después comentaron. El grupo en Facebook fue utilizado para la interacción social principalmente con otros estudiantes y el profesor, como un complemento a las sesiones presenciales. Los resultados obtenidos y el empleo de estrategias, puede ayudar a la concep - tualización del m-learning y mostrar como Facebook puede funcionar como un entorno de aprendizaje para apoyar la enseñanza y aprendizaje en el área del diseño.

Best practice report on policy learning infrastructures in innovative labour market policies

This report synthesizes the findings of 11 country reports on policy learning in labour market and social policies that were conducted as part of WP5 of the INSPIRES project, which is funded by the 7th Framework Program of the EU-Commission. Notably, this report puts forward objectives of policy learning, discusses tools, processes and institutions of policy learning and presents the impacts of various tools and structures of the policy learning infrastructure for the actual policy learning process. The report defines three objectives of policy learning: evaluation and assessment of policy effectiveness, vision building and planning, and consensus building. In the 11 countries under consideration, the tools and processes of the policy learning, infrastructure can be classified into three broad groups: public bodies, expert councils, and parties, interest groups and the private sector. Finally, we develop four recommendations for policy learning: Firstly, learning processes should keep the balance between centralisation and plurality. Secondly, learning processes should be kept stable beyond the usual political business cycles. Thirdly, policy learning tools and infrastructures should be sufficiently independent from political influence or bias. Fourth, Policy learning tools and infrastructures should balance out mere effectiveness, evaluation and vision building.

Students’ viewpoint on engineering subjects taught in English

The European Educational Institutions have the challenge and the commitment to enhance multilingual competence and teaching curricular subjects in a foreign language is seen as one of the most promising alternatives. In that context, professors teaching different engineering subjects at the School of Engineering of the UPC at Manresa (EPSEM) have been involved in projects aiming at analyzing the current linguistic situation and developing some on-line open access materials using CLIL as a strategy. They formed the u-Linguatech Research Group on Multilingual Communication in Science and Technology in order to provide such resources in an effective and efficient way. In this paper, we focus on students’ perception of the improvement of their multilingual competence throughout their Engineering degree, by means of subjects taught in English by non-native speakers. Data about the English level of current students are taken into account. We also describe the use of the above resources to improve the quality of subjects learning related to Chemical Engineering curricula.

Online Learning and Teaching in Higher Education, a book review

Peer-reviewed

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.

Requirement management in nuclear power projects

Diplomityön tarkoituksena oli tutkia vaatimusten hallintaa suunnittelu- ja konsultointiyrityksen kannalta Suomen ydinvoimaprojekteissa keskittyen ydinturvallisuus- ja laatuvaatimuksiin. Ydinvoimaprojekteissa toimiminen on edellyttänyt menettelyohjeiden ja laatujärjestelmän uudelleen organisointia yrityksessä ja esiin on noussut haasteita liittyen muun muassa vaatimusten tunnistamiseen ja todentamiseen erityyppisissä ja erilaajuisissa projekteissa. Työ toteutettiin perehtymällä ydinvoimaan liittyvään lainsäädäntöön Suomessa, ohjeisiin ja standardeihin sekä haastattelemalla yrityksen omia asiantuntijoita. Viimeaikaisista sekä meneillään olevista projekteista kerättiin kokemuksia sekä arvioitiin ydinvoima projekteja varten laaditun projektin toteutusohjeen toimivuutta ja käytettävyyttä esimerkkiprojektin avulla. Suurimmiksi haasteiksi tunnistettiin lainsäädännöllisten vaatimusten, kuten ydinvoima- laitosohjeiden (YVL) muuttuminen ja tulkinnanvaraisuus sekä asiakkaiden perehtymät- tömyys Suomen lainsäädäntöön ja vaatimustasoon liittyen ydinturvallisuuteen. Työn tuloksena tunnistettiin hyviä vaatimusten hallintaan liittyviä projektinhallintaa ja ydin- turvallisuutta edistäviä asioita, kuten vaatimusten täsmentäminen jo sopimustasolla sekä niiden täyttymisen seuranta projektin aikana. Erillisen vaatimustietokannan luomista ydinvoimaprojekteja varten tutkittiin, mutta siitä luovuttiin teknisten vaatimusten osalta kannattamattomana, sillä standardien ja vaatimusten määrä kasvoi niin suureksi, että niiden hallitseminen vaatisi enemmän työtä kuin mitä projektien taso yleensä sallisi.

Proactive and Preventive Student Welfare Activities in Finnish Preschool and Elementary School: Handling of Transition to Formal Schooling and a National Anti-Bullying Program as Examples

Prerequisites and effects of proactive and preventive psycho-social student welfare activities in Finnish preschool and elementary school were of interest in the present thesis. So far, Finnish student welfare work has mainly focused on interventions and individuals, and the voluminous possibilities to enhance well-being of all students as a part of everyday school work have not been fully exploited. Consequently, in this thesis three goals were set: (1) To present concrete examples of proactive and preventive psycho-social student welfare activities in Finnish basic education; (2) To investigate measurable positive effects of proactive and preventive activities; and (3) To investigate implementation of proactive and preventive activities in ecological contexts. Two prominent phenomena in preschool and elementary school years—transition to formal schooling and school bullying—were chosen as examples of critical situations that are appropriate targets for proactive and preventive psycho-social student welfare activities. Until lately, the procedures concerning both school transitions and school bullying have been rather problem-focused and reactive in nature. Theoretically, we lean on the bioecological model of development by Bronfenbrenner and Morris with concentric micro-, meso-, exo- and macrosystems. Data were drawn from two large-scale research projects, the longitudinal First Steps Study: Interactive Learning in the Child–Parent– Teacher Triangle, and the Evaluation Study of the National Antibullying Program KiVa. In Study I, we found that the academic skills of children from preschool–elementary school pairs that implemented several supportive activities during the preschool year developed more quickly from preschool to Grade 1 compared with the skills of children from pairs that used fewer practices. In Study II, we focused on possible effects of proactive and preventive actions on teachers and found that participation in the KiVa antibullying program influenced teachers‘ self-evaluated competence to tackle bullying. In Studies III and IV, we investigated factors that affect implementation rate of these proactive and preventive actions. In Study III, we found that principal‘s commitment and support for antibullying work has a clear-cut positive effect on implementation adherence of student lessons of the KiVa antibullying program. The more teachers experience support for and commitment to anti-bullying work from their principal, the more they report having covered KiVa student lessons and topics. In Study IV, we wanted to find out why some schools implement several useful and inexpensive transition practices, whereas other schools use only a few of them. We were interested in broadening the scope and looking at local-level (exosystem) qualities, and, in fact, the local-level activities and guidelines, along with teacherreported importance of the transition practices, were the only factors significantly associated with the implementation rate of transition practices between elementary schools and partner preschools. Teacher- and school-level factors available in this study turned out to be mostly not significant. To summarize, the results confirm that school-based promotion and prevention activities may have beneficial effects not only on students but also on teachers. Second, various top-down processes, such as engagement at the level of elementary school principals or local administration may enhance implementation of these beneficial activities. The main message is that when aiming to support the lives of children the primary focus should be on adults. In future, promotion of psychosocial well-being and the intrinsic value of inter- and intrapersonal skills need to be strengthened in the Finnish educational systems. Future research efforts in student welfare and school psychology, as well as focused training for psychologists in educational contexts, should be encouraged in the departments of psychology and education in Finnish universities. Moreover, a specific research centre for school health and well-being should be established.

A bridge between engineering and language learning : automation and German in an online course

Artikkeli luettavissa osassa: Part 2. - ISBN 9789522163172(PDF). - Liitteenä työpaperi