How Millennials are changing the way of learning: the state of the art of ICT integration in education

Millennials generation is changing the way of learning, prompting educational institutions to attempt to better adapt to young needs by incorporating technologies into education. Based on this premise, we have reviewed the prominent reports of the integration of ICT into education with the aim of evidencing how education is changing, and will change, to meet the needs ofMillennials with ICT support. We conclude that most of the investments have simply resulted in an increase of computers and access to the Internet, with teachers reproducing traditional approaches to education and e-learning being seen as complementary to face-to-face education. While it would seem that the use of ICT is not revolutionizing learning, it is facilitating the personalization, collaboration and ubiquity of learning.

Technology-enhanced education for Millennials: how the Information Society is changing the way of learning

The Information Society has provided the context for the development of a new generation, known as the Millennials, who are characterized by their intensive use of technologies in everyday life. These features are changing the way of learning, prompting educational institutions to attempt to better adapt to youngneeds by incorporating technologies into education. Based on this premise, wehave reviewed the prominent reports of the integration of ICT into education atdifferent levels with the aim of evidencing how education is changing, and willchange, to meet the needs of Millennials with ICT support. The results show thatmost of the investments have simply resulted in an increase of computers andaccess to the Internet, with teachers reproducing traditional approaches to education and e-learning being seen as complementary to face-to-face education.While it would seem that the use of ICT is not revolutionizing learning, it isfacilitating the personalization, collaboration and ubiquity of learning.

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

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.

Statewide Work-Based Learning Intermediary Network, FY2014, November 26, 2014

The Iowa Department of Education (DE) was appropriated $1.45 million for the development and implementation of a statewide work-based learning intermediary network. This funding was awarded on a competitive basis to 15 regional intermediary networks. Funds received by the regional intermediary networks from the state through this grant are to be used to develop and expand work-based learning opportunities within each region. A match of resources equal to 25 percent was a requirement of the funding. This match could include private donations, in-kind contributions, or public moneys. Funds may be used to support personnel responsible for the implementation of the intermediary network program components.

Virulent bacterial infection improves aversive learning performance in Drosophila melanogaster.

Virulent infections are expected to impair learning ability, either as a direct consequence of stressed physiological state or as an adaptive response that minimizes diversion of energy from immune defense. This prediction has been well supported for mammals and bees. Here, we report an opposite result in Drosophila melanogaster. Using an odor-mechanical shock conditioning paradigm, we found that intestinal infection with bacterial pathogens Pseudomonas entomophila or Erwinia c. carotovora improved flies' learning performance after a 1h retention interval. Infection with P. entomophila (but not E. c. carotovora) also improved learning performance after 5 min retention. No effect on learning performance was detected for intestinal infections with an avirulent GacA mutant of P. entomophila or for virulent systemic (hemocoel) infection with E. c. carotovora. Assays of unconditioned responses to odorants and shock do not support a major role for changes in general responsiveness to stimuli in explaining the changes in learning performance, although differences in their specific salience for learning cannot be excluded. Our results demonstrate that the effects of pathogens on learning performance in insects are less predictable than suggested by previous studies, and support the notion that immune stress can sometimes boost cognitive abilities.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Learning in the working place: the educational potential of a multihead microscope in pathology postgraduate training.

Training future pathologists is an important mission of many hospital anatomic pathology departments. Apprenticeship-a process in which learning and teaching tightly intertwine with daily work, is one of the main educational methods in use in postgraduate medical training. However, patient care, including pathological diagnosis, often comes first, diagnostic priorities prevailing over educational ones. Recognition of the unique educational opportunities is a prerequisite for enhancing the postgraduate learning experience. The aim of this paper is to draw attention of senior pathologists with a role as supervisor in postgraduate training on the potential educational value of a multihead microscope, a common setting in pathology departments. After reporting on an informal observation of senior and junior pathologists' meetings around the multihead microscope in our department, we review the literature on current theories of learning to provide support to the high potential educational value of these meetings for postgraduate training in pathology. We also draw from the literature on learner-centered teaching some recommendations to better support learning in this particular context. Finally, we propose clues for further studies and effective instruction during meetings around a multihead microscope.

Open Educational Resources: an opportunity for virtual learning institutions

The Universitat Oberta de Catalunya (UOC, Open University of Catalonia) is involved inseveral research projects and educational activities related to the use of Open Educational Resources (OER). Some of the discussed issues in the concept of OER are research issues which are being tackled in two EC projects (OLCOS and SELF). Besides the research part, the UOC aims at developing a virtual centre for analysing and promoting the concept of OERin Europe in the sector of Higher and Further Education. The objectives are to makeinformation and learning services available to provide university management staff,eLearning support centres, faculty and learners with practical information required to create, share and re-use such interoperable digital content, tools and licensing schemes. In the realisation of these objectives, the main activities are the following: to provide organisationaland individual e-learning end-users with orientation; to develop perspectives and useful recommendations in the form of a medium-term Roadmap 2010 for OER in Higher and Further Education in Europe; to offer practical information and support services about how to create, share and re-use open educational content by means of tutorials, guidelines, best practices, and specimen of exemplary open e-learning content; to establish a larger group ofcommitted experts throughout Europe and other continents who not only share theirexpertise but also steer networking, workshops, and clustering efforts; and to foster and support a community of practice in open e-learning content know-how and experiences.

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.

Drosophila rely on learning while foraging under semi-natural conditions

Learning is predicted to affect manifold ecological and evolutionary processes, but the extent to which animals rely on learning in nature remains poorly known, especially for short-lived non-social invertebrates. This is in particular the case for Drosophila, a favourite laboratory system to study molecular mechanisms of learning. Here we tested whether Drosophila melanogaster use learned information to choose food while free-flying in a large greenhouse emulating the natural environment. In a series of experiments flies were first given an opportunity to learn which of two food odours was associated with good versus unpalatable taste; subsequently, their preference for the two odours was assessed with olfactory traps set up in the greenhouse. Flies that had experienced palatable apple-flavoured food and unpalatable orange-flavoured food were more likely to be attracted to the odour of apple than flies with the opposite experience. This was true both when the flies first learned in the laboratory and were then released and recaptured in the greenhouse, and when the learning occurred under free-flying conditions in the greenhouse. Furthermore, flies retained the memory of their experience while exploring the greenhouse overnight in the absence of focal odours, pointing to the involvement of consolidated memory. These results support the notion that even small, short lived insects which are not central-place foragers make use of learned cues in their natural environments.

The power of support: an exploration of the mentoring relationship

Tutkimuksen pääasiallisena tavoitteena on selvittää ne moninaiset näkökulmat, joita mentori sekä mentoroitava läpikäyvät osallistuessaan mentorointiohjelmaan. Tutkimuksen avulla pyritään myös selvittämään vastaavanlaisen tuen saannin tehokkuutta sekä mentoroinnin sisältäminen elementtien hyödyllisyyttä. Pyrkimyksenä on myös selvittää sekä ymmärtää ne dynaamiset mentorointiprosessin ominaisuudet molempien prosessiin osallistuvien tahojen näkökulmasta. Tutkimuksen teoriaosuudessa käsitellään mentorointia, mentorointiin liittyviä vuorovaikutussuhteita sekä näihin liittyviä eri näkökulmia. Teoria koostuu monipuolisesta, kansainvälisestä sekä kotimaisesta kirjallisuudesta. Tutkimuksen aineiston keruu suoritetaan puolistrukturoiduilla avoimilla haastatteluilla, jotka suoritetaan järjestämällä kahdenkeskisiä teemahaastatteluita. Mentorointisuhteella on todettu olevan voimakas vaikutus siihen osallistuvien henkilöiden elämään. Mentorointisuhteen tarkoituksellisuus on tullut todettua myös kun itse vuorovaikutussuhde on osoittautunut hyödylliseksi osallistujille. Mentoroitavilla on odotuksia, että mentorit täyttävät seuraavanlaisia rooleja: neuvonantaja, roolimalli sekä mahdollisesti tukea antava ystävä. Kaikissa läpikäydyissä tapauksissa mentori pystyi tarjoamaan jonkun näistärooleista mentoroitavalle. Mentoroitava, puolestaan, otti tämän tuen vastaan jahyötyi siitä.

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.

Using a blog to create and support a Community of Inquiry in Secondary Education

Understanding how blogs can support collaborative learning is a vital concern for researchers and teachers. This paper explores how blogs may be used to support Secondary Education students’ collaborative interaction and how such an interaction process can promote the creation of a Community of Inquiry to enhance critical thinking and meaningful learning. We designed, implemented and evaluated a science case-based project in which fifteen secondary students participated. Students worked in the science blogging project during 4 months. We asked students to be collaboratively engaged in purposeful critical discourse and reflection in their blogs in order to solve collectively science challenges and construct meaning about topics related to Astronomy and Space Sciences. Through student comments posted in the blog, our findings showed that the blog environment afforded the construction of a Community of Inquiry and therefore the creation of an effective online collaborative learning community. In student blog comments, the three presences for collaborative learning took place: cognitive, social, and teaching presence. Moreover, our research found a positive correlation among the three presences –cognitive, social and teaching– of the Community of Inquiry model with the level of learning obtained by the students. We discuss a series of issues that instructors should consider when blogs are incorporated into teaching and learning. We claim that embedded scaffolds to help students to argue and reason their comments in the blog are required to foster blog-supported collaborative learning.