The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

Learning about Bayesian networks for forensic interpretation : An example based on the "the problem of multiple propositions"

Both, Bayesian networks and probabilistic evaluation are gaining more and more widespread use within many professional branches, including forensic science. Notwithstanding, they constitute subtle topics with definitional details that require careful study. While many sophisticated developments of probabilistic approaches to evaluation of forensic findings may readily be found in published literature, there remains a gap with respect to writings that focus on foundational aspects and on how these may be acquired by interested scientists new to these topics. This paper takes this as a starting point to report on the learning about Bayesian networks for likelihood ratio based, probabilistic inference procedures in a class of master students in forensic science. The presentation uses an example that relies on a casework scenario drawn from published literature, involving a questioned signature. A complicating aspect of that case study - proposed to students in a teaching scenario - is due to the need of considering multiple competing propositions, which is an outset that may not readily be approached within a likelihood ratio based framework without drawing attention to some additional technical details. Using generic Bayesian networks fragments from existing literature on the topic, course participants were able to track the probabilistic underpinnings of the proposed scenario correctly both in terms of likelihood ratios and of posterior probabilities. In addition, further study of the example by students allowed them to derive an alternative Bayesian network structure with a computational output that is equivalent to existing probabilistic solutions. This practical experience underlines the potential of Bayesian networks to support and clarify foundational principles of probabilistic procedures for forensic evaluation.

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.

A methodological approach to identify teacher's ICT competencies in online learning environments in Higher Education

Aquest article pretén descriure el procés metodològic d'identificació i mesurament de les competències TIC dels professors i com a formadors en les TIC en un entorn d'aprenentatge en línia en l'Educació Superior portat a terme en el marc del Projecte Europeu Elene-TLC.La revisió de la recerca en les competències en línia del professor demostra que, en primer lloc, el mètode més utilitzat per a identificar aquestes competències és el focus group. En segon lloc, la tècnica Delphi és la tècnica més utilitzada per reunir el consens d'experts sobre quines són les competències principals per al professor en línia entre els que s'indiquen.La proposta metodològica descrita en aquest document consisteix en la creació de 7 grups de discussió en línia, l'objectiu dels quals era identificar les competències formatives dels professors en línia i les dels professos en línia. La llista de competències obtingudes posteriorment es va oferir als experts europeus que participaven en l'aplicació de la tècnica Delphi. A aquests experts se'ls va demanar que ordenessin les competències d'acord amb el seu grau d'importància.Els resultats mostren que els grups de discussió en línia i el mètode Delphi són les metodologies apropiades per a identificar les competències TIC dels professors universitaris en els entorns d'aprenentatge en línia.

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.

Developing Processes and Learning in a Project-based Firm

Tämän diplomityön tavoitteena on kuvata tiedonkulkua projektiliiketoimintaa harjoittavassa yrityksessä sekä analysoida kuvausta määrittäen mahdolliset kehityskohdat. Työssätuotetut kuvaukset ja kehityskohtien määrittäminen toimivat pohjana yrityksen kehittäessä projektien hallintaansa tulevaisuudessa. Työssä valitaan tietojohtamisen näkökulma sopivaksi lähestymistavaksi yrityksen toiminnananalysointiin. Haastatteluin kerätyn tutkimusmateriaalin perusteella luodaan prosessikuvaukset jotka mallintavat tietovirtoja yrityksen projektien aikana tapahtuvien prosessien välillä. Kuvausta peilataan tietämyksen luomisen sekä projektien tietojohtamisen teoriaan ja määritetään kehityskohteita. Kehityskohteiden määrittämisen lisäksi ehdotetaan mahdollisia toimenpiteitä tiedon ja tietämyksen hallinnan kehittämiseksi. Kokemusten ja opittujen asioiden sekäpalautteen kerääminen projektien aikana sekä niiden jälkeen havaittiin tärkeimmäksi kehityskohdaksi. Näiden keräämisen voidaan todeta vaativan järjestelmällisyyttä jotta projektien onnistumiset sekä niissä saavutetut parannukset voidaan toistaa jatkossa ja virheet sekä epäonnistumiset sitä vastoin välttää.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

La Incidencia de las nuevas tecnologías en los hábitos formativos de los estudiantes de magisterio = The Impact of new technologies in the learning habits of teacher students

Este trabajo persigue dos objetivos: el primero es analizar el uso de las TIC en un grupo de estudiantes de segundo curso de Magisterio de la Universidad de Girona; el segundo es analizar los documentos normativos legales que establecen el currículum de educación primaria en Cataluña para observar qué tipo de papel juegan las TIC en las nuevas programaciones educativas. La primera parte se ha llevado a cabo mediante una encuesta, cuyos resultados permiten observar tres aspectos distintos: el primero, que una parte considerable del grupo considera las TIC más como un complemento para el aprendizaje que como una forma de aprendizaje; el segundo, que a pesar de hacer un uso considerable de las TIC, el conocimiento que tienen de ellas es muy básico y utilizan aplicaciones muy genéricas; y el tercero es que una parte de sus propuestas didácticas para el uso de las TIC son propuestas tradicionales simplemente adaptadas a un nuevo instrumento, sin buscar realmente la innovación que puede suponer la incorporación de las TIC. En la segunda parte del artículo, a partir del análisis e interpretación de los documentos legales que establecen el currículum de Educación Primaria, se observa que en un mismo documento conviven aserciones sobre las TIC como complemento al aprendizaje de contenidos con otras formulaciones que consideran las TIC como constructoras de conocimiento. A partir del perfil de los estudiantes y del estado de los documentos legales, al final del artículo se hacen propuestas para formar al futuro maestro teniendo en cuenta las TIC como herramientas básicas de conocimiento

The effects of ICT on school: teachers’ and students’ perspectives

The purpose of this study was to investigate the effects of information and communication technology (ICT) on school from teachers’ and students’ perspectives. The focus was on three main subject matters: on ICT use and competence, on teacher and school community, and on learning environment and teaching practices. The study is closely connected to the national educational policy which has aimed strongly at supporting the implementation of ICT in pedagogical practices at all institutional levels. The phenomena were investigated using a mixed methods approach. The qualitative data from three cases studies and the quantitative data from three statistical studies were combined. In this study, mixed methods were used to investigate the complex phenomena from various stakeholders’ points of view, and to support validation by combining different perspectives in order to give a fuller and more complete picture of the phenomena. The data were used in a complementary manner. The results indicate that the technical resources for using ICT both at school and at homes are very good. In general, students are capable and motivated users of new technology; these skills and attitudes are mainly based on home resources and leisuretime use. Students have the skills to use new kinds of applications and new forms of technology, and their ICT skills are wide, although not necessarily adequate; the working habits might be ineffective and even wrong. Some students have a special kind of ICT-related adaptive expertise which develops in a beneficial interaction between school guidance and challenges, and individual interest and activity. Teachers’ skills are more heterogeneous. The large majority of teachers have sufficient skills for everyday and routine working practices, but many of them still have difficulties in finding a meaningful pedagogical use for technology. The intensive case study indicated that for the majority of teachers the intensive ICT projects offer a possibility for learning new skills and competences intertwined in the work, often also supported by external experts and a collaborative teacher community; a possibility that “ordinary” teachers usually do not have. Further, teachers’ good ICT competence help them to adopt new pedagogical practices and integrate ICT in a meaningful way. The genders differ in their use of and skills in ICT: males show better skills especially in purely technical issues also in schools and classrooms, whereas female students and younger female teachers use ICT in their ordinary practices quite naturally. With time, the technology has become less technical and its communication and creation affordances have become stronger, easier to use, more popular and motivating, all of which has increased female interest in the technology. There is a generation gap in ICT use and competence between teachers and students. This is apparent especially in the ICT-related pedagogical practices in the majority of schools. The new digital affordances not only replace some previous practices; the new functionalities change many of our existing conceptions, values, attitudes and practices. The very different conceptions that generations have about technology leads, in the worst case, to a digital gap in education; the technology used in school is boring and ineffective compared to the ICT use outside school, and it does not provide the competence needed for using advanced technology in learning. The results indicate that in schools which have special ICT projects (“ICT pilot schools”) for improving pedagogy, these have led to true changes in teaching practices. Many teachers adopted student-centred and collaborative, inquiry-oriented teaching practices as well as practices that supported students' authentic activities, independent work, knowledge building, and students' responsibility. This is, indeed, strongly dependent on the ICT-related pedagogical competence of the teacher. However, the daily practices of some teachers still reflected a rather traditional teacher-centred approach. As a matter of fact, very few teachers ever represented solely, e.g. the knowledge building approach; teachers used various approaches or mixed them, based on the situation, teaching and learning goals, and on their pedagogical and technical competence. In general, changes towards pedagogical improvements even in wellorganised developmental projects are slow. As a result, there are two kinds of ICT stories: successful “ICT pilot schools” with pedagogical innovations related to ICT and with school community level agreement about the visions and aims, and “ordinary schools”, which have no particular interest in or external support for using ICT for improvement, and in which ICT is used in a more routine way, and as a tool for individual teachers, not for the school community.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Organisational learning and knowledge acquisition. A case study of internationalising manufacturers of wood-based prefabricated buildings

The study investigates organisational learning and knowledge acquisition of wood-based prefabricated building manufacturers. This certain group of case companies was chosen, because their management and their employees generally have a strong manufacturing and engineering background, while the housing sector is characterised by national norms, regulations, as well as local building styles. Considering this setting, it was investigated, how the case companies develop organisational learning capabilities, acquire and transfer knowledge for their internationalisation. The theoretical framework of this study constitutes the knowledge-based conceptualisation of internationalisation, which combines the traditional internationalisation process, as well as the international new venture perspective based on their commonalities in the knowledge-based view of the firm. Different theories of internationalisation, including the network-perspective, were outlined and a framework on organisational learning and knowledge acquisition was established. The empirical research followed a qualitative approach, deploying a multiple-case study with five case companies from Austria, Finland and Germany. In the study, the development of the wood-based prefabricated building industry and of the case companies are described, and the motives, facilitators and challenges for foreign expansion, as well as the companies’ internationalisation approaches are compared. Different methods of how companies facilitate the knowledge-exchange or learn about new markets are also outlined. Experience, market knowledge and personal contacts are considered essential for the internationalisation process. The major finding of the study is that it is not necessary to acquire the market knowledge internally in a slow process as proposed by the Uppsala model. In four cases companies engaged knowledge in symbiotic relations with local business partners. Thereby, the building manufacturers contribute their design and production capabilities, and in return, their local partners provide them with knowledge about the market and local regulations; while they manage the sales and construction operations. Thus, the study provides strong evidence for the propositions of network perspective. One case company developed the knowledge internally in a gradual process: it entered the market sequentially with several business lines, showing an increasing level of complexity. In both of the observed strategies, single-loop and double-loop learning processes occurred.

ICT-palveluiden asiakaslähtöinen tuotteistaminen, case Valtion IT-palvelukeskus

Syksyllä 2012 Valtion IT-palvelukeskuksen teettämässä asiakastyytyväisyyskyselyssä alle puolet vastanneista oli tyytyväisiä palveluiden tuotteistamiseen, joka heijastui asiakkaan kokemaan palvelun arvoon. Tutkimuksen tavoitteena oli selvittää valtionhallinnon asiakkaille tarjottavien ICT-palveluiden asiakaslähtöiseen tuotteistamiseen vaikuttavat oleelliset osa-alueet. Tutkimus oli luonteeltaan kvalitatiivinen ja tutkimusmenetelmänä käytettiin grounded theory -menetelmää. Tutkimusaineisto koostui 11 johtajahaastattelusta. Tutkimustuloksia käsiteltiin kolmessa teemassa: ICT-palvelu, tuotteistaminen ja asiakaslähtöisyys. Tutkimustulosten perusteella asiakaslähtöiseen tuotteistukseen vaikuttavia tekijöitä ovat yhteinen tavoite ja yhteistyö; asiakasymmärrys, hyödyt ja lisäarvo, laatu ja luottamus, osaaminen ja oppiminen, vastuut ja roolit sekä vuorovaikutus. Tutkimustulokset antavat hyvän lähtökohdan asiakaslähtöisen tuotteistuksen kehittämiseen ja palveluiden laadun parantamisen kehitystoimenpiteiden suunnitteluun.