Iowa Sex Offender Treatment and Supervision Task Force Report to the Iowa General Assembly, January 15, 2007

Over the last several years, lawmakers have been responding to several highly publicized child abduction, assault and murder cases. While such cases remain rare in Iowa, the public debates they have generated are having far-reaching effects. Policy makers are responsible for controlling the nature of such effects. Challenges they face stem from the need to avoid primarily politically-motivated responses and the desire to make informed decisions that recognize both the strengths and the limitations of the criminal justice system as a vehicle for promoting safe and healthy families and communities. Consensus was reached by the Task Force at its first meeting that one of its standing goals is to provide nonpartisan guidance to help avoid or fix problematic sex offense policies and practices. Setting this goal was a response to the concern over what can result from elected officials’ efforts to respond to the types of sex offender-related concerns that can easily become emotionally laden and politically charged due to the universally held abhorrence of sex crimes against children. The meetings of the Task Force and the various work groups it has formed have included some spirited and perhaps emotionally charged discussions, despite the above-stated ground rule. However, as is described in the report, the Task Force’s first set of recommendations and plans for further study were approved through consensus. It is hoped that in upcoming legislative deliberations, it will be remembered that the non-legislative members of the Task Force all agreed on the recommendations contained in this report. The topics discussed in this first report from the Task Force are limited to the study issues specifically named in H.F. 619, the Task Force’s enabling legislation. However, other topics of concern were discussed by the Task Force because of their immediacy or because of their possible relationships with one or more of the Task Force’s mandated study issues. For example, it has been reported by some probation/parole officers and others that the 2000 feet rule has had a negative influence on treatment participation and supervision compliance. While such concerns were noted, the Task Force did not take it upon itself to investigate them at this time and thus broaden the agenda it was given by the General Assembly last session. As a result, the recently reinstated 2000 feet rule, the new cohabitation/child endangerment law and other issues of interest to Task Force members but not within the scope of their charge are not discussed in the body of this report. An issue of perhaps the greatest interest to most Task Force members that was not a part of their charge was a belief in the benefit of viewing Iowa’s efforts to protect children from sex crimes with as comprehensive a platform as possible. It has been suggested that much more can be done to prevent child-victim sex crimes than would be accomplished by only concentrating on what to do with offenders after a crime has occurred. To prevent child victimization, H.F. 619 policy provisions rely largely on incapacitation and future deterrent effects of increased penalties, more restrictive supervision practices and greater public awareness of the risk presented by a segment of Iowa’s known sex offenders. For some offenders, these policies will no doubt prevent future sex crimes against children, and the Task Force has begun long-term studies to look for the desired results and for ways to improve such results through better supervision tools and more effective offender treatment. Unfortunately, much of the effects from the new policies may primarily influence persons who have already committed sex offenses against minors and who have already been caught doing so. Task Force members discussed the need for a range of preventive efforts and a need to think about sex crimes against children from other than just a “reaction- to-the-offender” perspective. While this topic is not addressed in the report that follows, it was suggested that some of the Task Force’s discussions could be briefly shared through these opening comments. Along with incapacitation and deterrence, comprehensive approaches to the prevention of child-victim sex crimes would also involve making sure parents have the tools they need to detect signs of adults with sex behavior problems, to help teach their children about warning signs and to find the support they need for healthy parenting. School, faithbased and other community organizations might benefit from stronger supports and better tools they can use to more effectively promote positive youth development and the learning of respect for others, respect for boundaries and healthy relationships. All of us who have children, or who live in communities where there are children, need to understand the limitations of our justice system and the importance of our own ability to play a role in preventing sexual abuse and protecting children from sex offenders, which are often the child’s own family members. Over 1,000 incidences of child sexual abuse are confirmed or founded each year in Iowa, and most such acts take place in the child’s home or the residence of the caretaker of the child. Efforts to prevent child sexual abuse and to provide for early interventions with children and families at risk could be strategically examined and strengthened. The Sex Offender Treatment and Supervision Task Force was established to provide assistance to the General Assembly. It will respond to legislative direction for adjusting its future plans as laid out in this report. Its plans could be adjusted to broaden or narrow its scope or to assign different priority levels of effort to its current areas of study. Also, further Task Force considerations of the recommendations it has already submitted could be called for. In the meantime, it is hoped that the information and recommendations submitted through this report prove helpful.

Iowa Sex Offender Treatment and Supervision Task Force Report to the Iowa General Assembly, January 15, 2008

Over the last several years, lawmakers have been responding to several highly publicized child abduction, assault and murder cases. While such cases remain rare in Iowa, the public debates they have generated are having far-reaching effects. Policy makers are responsible for controlling the nature of such effects. Challenges they face stem from the need to avoid primarily politically-motivated responses and the desire to make informed decisions that recognize both the strengths and the limitations of the criminal justice system as a vehicle for promoting safe and healthy families and communities. Consensus was reached by the Task Force at its first meeting that one of its standing goals is to provide nonpartisan guidance to help avoid or fix problematic sex offense policies and practices. Setting this goal was a response to the concern over what can result from elected officials’ efforts to respond to the types of sex offender-related concerns that can easily become emotionally laden and politically charged due to the universally held abhorrence of sex crimes against children. The meetings of the Task Force and the various work groups it has formed have included some spirited and perhaps emotionally charged discussions, despite the above-stated ground rule. However, as is described in the report, the Task Force’s first set of recommendations and plans for further study were approved through consensus. It is hoped that in upcoming legislative deliberations, it will be remembered that the non-legislative members of the Task Force all agreed on the recommendations contained in this report. The topics discussed in this first report from the Task Force are limited to the study issues specifically named in H.F. 619, the Task Force’s enabling legislation. However, other topics of concern were discussed by the Task Force because of their immediacy or because of their possible relationships with one or more of the Task Force’s mandated study issues. For example, it has been reported by some probation/parole officers and others that the 2000 feet rule has had a negative influence on treatment participation and supervision compliance. While such concerns were noted, the Task Force did not take it upon itself to investigate them at this time and thus broaden the agenda it was given by the General Assembly last session. As a result, the recently reinstated 2000 feet rule, the new cohabitation/child endangerment law and other issues of interest to Task Force members but not within the scope of their charge are not discussed in the body of this report. An issue of perhaps the greatest interest to most Task Force members that was not a part of their charge was a belief in the benefit of viewing Iowa’s efforts to protect children from sex crimes with as comprehensive a platform as possible. It has been suggested that much more can be done to prevent child-victim sex crimes than would be accomplished by only concentrating on what to do with offenders after a crime has occurred. To prevent child victimization, H.F. 619 policy provisions rely largely on incapacitation and future deterrent effects of increased penalties, more restrictive supervision practices and greater public awareness of the risk presented by a segment of Iowa’s known sex offenders. For some offenders, these policies will no doubt prevent future sex crimes against children, and the Task Force has begun long-term studies to look for the desired results and for ways to improve such results through better supervision tools and more effective offender treatment. Unfortunately, much of the effects from the new policies may primarily influence persons who have already committed sex offenses against minors and who have already been caught doing so. Task Force members discussed the need for a range of preventive efforts and a need to think about sex crimes against children from other than just a “reaction- to-the-offender” perspective. While this topic is not addressed in the report that follows, it was suggested that some of the Task Force’s discussions could be briefly shared through these opening comments. Along with incapacitation and deterrence, comprehensive approaches to the prevention of child-victim sex crimes would also involve making sure parents have the tools they need to detect signs of adults with sex behavior problems, to help teach their children about warning signs and to find the support they need for healthy parenting. School, faithbased and other community organizations might benefit from stronger supports and better tools they can use to more effectively promote positive youth development and the learning of respect for others, respect for boundaries and healthy relationships. All of us who have children, or who live in communities where there are children, need to understand the limitations of our justice system and the importance of our own ability to play a role in preventing sexual abuse and protecting children from sex offenders, which are often the child’s own family members. Over 1,000 incidences of child sexual abuse are confirmed or founded each year in Iowa, and most such acts take place in the child’s home or the residence of the caretaker of the child. Efforts to prevent child sexual abuse and to provide for early interventions with children and families at risk could be strategically examined and strengthened. The Sex Offender Treatment and Supervision Task Force was established to provide assistance to the General Assembly. It will respond to legislative direction for adjusting its future plans as laid out in this report. Its plans could be adjusted to broaden or narrow its scope or to assign different priority levels of effort to its current areas of study. Also, further Task Force considerations of the recommendations it has already submitted could be called for. In the meantime, it is hoped that the information and recommendations submitted through this report prove helpful.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Supporting interdisciplinary case studies: development and implementation of a joint learning environment for students in forensic science and criminal law

Scientific reporting and communication is a challenging topic for which traditional study programs do not offer structured learning activities on a regular basis. This paper reports on the development and implementation of a web application and associated learning activities that intend to raise the awareness of reporting and communication issues among students in forensic science and law. The project covers interdisciplinary case studies based on a library of written reports about forensic examinations. Special features of the web framework, in particular a report annotation tool, support the design of various individual and group learning activities that focus on the development of knowledge and competence in dealing with reporting and communication challenges in the students' future areas of professional activity.

Information literacy - historical and conceptual bases: constructing meanings

Recent years have witnessed the increasing interest in studies focused on information literacy, which is reflected mainly in the number of publications on the subject and goes beyond the fields of Librarianship and Information Science. The purpose of this paper is, therefore, to offer an outlook, historical and conceptual, of international researches on information literacy, trying to show some of the different ramifications which the discussion on the subject has exhibited in past few years in countries where its process of legitimation is already well established, in order to illuminate possible areas of research and action for the librarian professional. This research indicates that if the initial studies on this topic tended to be devoted to conceptualize it, discussing its relevance and determine the skills and knowledge related to information literacy, in the last decade can be noticed a proliferation of researches aimed at describing initiatives or proposing models in areas beyond the usual field such as Medical Sciences, Law, Politics or Computers, among others. The first results of this research refer to a philosophical and educational perspective of information literacy, which suggests the need for deeper understanding and characterization of information literacy in four dimensions: technical, aesthetic, ethical and political, serving both to competence as to information.

Glucose-dependent insulinotropic polypeptide receptor knockout mice are impaired in learning, synaptic plasticity, and neurogenesis.

Glucose-dependent insulinotropic polypeptide (GIP) is a key incretin hormone, released from intestine after a meal, producing a glucose-dependent insulin secretion. The GIP receptor (GIPR) is expressed on pyramidal neurons in the cortex and hippocampus, and GIP is synthesized in a subset of neurons in the brain. However, the role of the GIPR in neuronal signaling is not clear. In this study, we used a mouse strain with GIPR gene deletion (GIPR KO) to elucidate the role of the GIPR in neuronal communication and brain function. Compared with C57BL/6 control mice, GIPR KO mice displayed higher locomotor activity in an open-field task. Impairment of recognition and spatial learning and memory of GIPR KO mice were found in the object recognition task and a spatial water maze task, respectively. In an object location task, no impairment was found. GIPR KO mice also showed impaired synaptic plasticity in paired-pulse facilitation and a block of long-term potentiation in area CA1 of the hippocampus. Moreover, a large decrease in the number of neuronal progenitor cells was found in the dentate gyrus of transgenic mice, although the numbers of young neurons was not changed. Together the results suggest that GIP receptors play an important role in cognition, neurotransmission, and cell proliferation.

Transmission electron microscopy in cell biology: sample preparation techniques and image information

Transmission electron microscopy is a proven technique in the field of cell biology and a very useful tool in biomedical research. Innovation and improvements in equipment together with the introduction of new technology have allowed us to improve our knowledge of biological tissues, to visualizestructures better and both to identify and to locate molecules. Of all the types ofmicroscopy exploited to date, electron microscopy is the one with the mostadvantageous resolution limit and therefore it is a very efficient technique fordeciphering the cell architecture and relating it to function. This chapter aims toprovide an overview of the most important techniques that we can apply to abiological sample, tissue or cells, to observe it with an electron microscope, fromthe most conventional to the latest generation. Processes and concepts aredefined, and the advantages and disadvantages of each technique are assessedalong with the image and information that we can obtain by using each one ofthem.

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.

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.

Remote collaborative multi-user informal learning experiences: design and evaluation

This paper presents a customizable system used to develop a collaborative multi-user problem solving game. It addresses the increasing demand for appealing informal learning experiences in museum-like settings. The system facilitates remote collaboration by allowing groups of learners tocommunicate through a videoconferencing system and by allowing them to simultaneously interact through a shared multi-touch interactive surface. A user study with 20 user groups indicates that the game facilitates collaboration between local and remote groups of learners. The videoconference and multitouch surface acted as communication channels, attracted students’ interest, facilitated engagement, and promoted inter- and intra-group collaboration—favoring intra-group collaboration. Our findings suggest that augmentingvideoconferencing systems with a shared multitouch space offers newpossibilities and scenarios for remote collaborative environments and collaborative learning.

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.

Improving Searching and Browsing Capabilities of Learning Object Repositories

Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative meta-data used for describing content, such as author, title or publicationdate, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services fordescribing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative filtering techniques for improving the user interface of a DSpace based learning object repository.

Extending Learning Objects by Means of Social Networking

Learning objects have been the promise of providing people with high quality learning resources. Initiatives such as MIT Open-CourseWare, MERLOT and others have shown the real possibilities of creating and sharing knowledge through Internet. Thousands of educational resources are available through learning object repositories. We indeed live in an age of content abundance, and content can be considered as infrastructure for building adaptive and personalized learning paths, promoting both formal and informal learning. Nevertheless, although most educational institutions are adopting a more open approach, publishing huge amounts of educational resources, the reality is that these resources are barely used in other educational contexts. This paradox can be partly explained by the dificulties in adapting such resources with respect to language, e-learning standards and specifications and, finally, granularity. Furthermore, if we want our learners to use and take advantage of learning object repositories, we need to provide them with additional services than just browsing and searching for resources. Social networks can be a first step towards creating an open social community of learning around a topic or a subject. In this paper we discuss and analyze the process of using a learning object repository and building a social network on the top of it, with respect to the information architecture needed to capture and store the interaction between learners and resources in form of learning object metadata.

Open Apps: Opening Teaching Experiences and Applications

The Universitat Oberta de Catalunya (Open University of Catalonia, UOC) is an online university that makes extensive use of information and communication technologies to provide education. Ever since its establishment in 1995, the UOC has developed and tested methodologies and technological support services to meet the educational challenges posed by its student community and its teaching and management staff. The know-how it has acquired in doing so is the basis on which it has created the Open Apps platform, which is designed to provide access to open source technical applications, information on successful learning and teaching experiences, resources and other solutions, all in a single environment. Open Apps is an open, online catalogue, the content of which is available to all students for learning purposes, all IT professionals for downloading and all teachers for reusing.To contribute to the transfer of knowledge, experience and technology, each of the platform¿s apps comes with full documentation, plus information on cases in which it has been used and related tools. It is hoped that such transfer will lead to the growth of an external partner network, and that this, in turn, will result in improvements to the applications and teaching/learning practices, and in greater scope for collaboration.Open Apps is a strategic project that has arisen from the UOC's commitment to the open access movement and to giving knowledge and technology back to society, as well as its firm belief that sustainability depends on communities of interest.

Learning in Product Development Projects - Responding to challenges in global industrial company

Verkostoitunut kansainvälinen tuotekehitys on tärkeä osa menestystä nykypäivän muuttuvassa yritysmaailmassa. Toimintojen tehostamiseksi myös projektitoiminnot on sopeutettava kansainväliseen toimintaympäristöön. Kilpailukyvyn säilyttämiseksi projektitoimintoja on lisäksi jatkuvasti tehostettava. Yhtenäkeinona nähdään projektioppiminen, jota voidaan edistää monin eri tavoin. Tässätyössä keskitytään projektitiedonhallinnan kehittämisen tuomiin oppimismahdollisuuksiin. Kirjallisuudessa kerrotaan, että projektitiedon jakaminen ja sen hyödyntäminen seuraavissa projekteissa on eräs projektioppimisen edellytyksistä. Tämäon otettu keskeiseksi näkökulmaksi tässä tutkimuksessa. Lisäksi tutkimusalueen rajaamiseksi työ tarkastelee erityisesti projektioppimista kansainvälisten tuotekehitysprojektien välillä. Työn tavoitteena on esitellä keskeisiä projektioppimisen haasteita ja etsiä konkreettinen ratkaisu vastaamaan näihin haasteisiin. Tuotekehitystoiminnot ja kansainvälinen hajautettu projektiorganisaatio kohtaavat lisäksi erityisiä haasteita, kuten tiedon hajautuneisuus, projektihenkilöstön vaihtuvuus, tiedon luottamuksellisuus ja maantieteelliset haasteet (esim. aikavyöhykkeet ja toimipisteen sijainti). Nämä erityishaasteet on otettu huomioon ratkaisua etsittäessä. Haasteisiin päädyttiin vastaamaan tietotekniikkapohjaisella ratkaisulla, joka suunniteltiin erityisesti huomioiden esimerkkiorganisaation tarpeet ja haasteet. Työssä tarkastellaan suunnitellun ratkaisun vaikutusta projektioppimiseen ja kuinka se vastaa havaittuihin haasteisiin. Tuloksissa huomattiin, että projektioppimista tapahtui, vaikka oppimista oli vaikea suoranaisesti huomata tutkimusorganisaation jäsenten keskuudessa. Projektioppimista voidaan kuitenkin sanoa tapahtuvan, jos projektitieto on helposti koko projektiryhmän saatavilla ja se on hyvin järjesteltyä. Muun muassa nämä ehdot täyttyivät. Projektioppiminen nähdään yleisesti haastavana kehitysalueena esimerkkiorganisaatiossa. Suuri osa tietämyksestä on niin sanottua hiljaistatietoa, jota on hankala tai mahdoton saattaa kirjalliseen muotoon. Näin olleen tiedon siirtäminen jää suurelta osin henkilökohtaisen vuorovaikutuksen varaan. Siitä huolimatta projektioppimista on mahdollista kehittää erilaisin toimintamallein ja menetelmin. Kehitys vaatii kuitenkin resursseja, pitkäjänteisyyttä ja aikaa. Monet muutokset voivat vaatia myös organisaatiokulttuurin muutoksen ja vaikuttamista organisaation jäseniin. Motivaatio, positiiviset mielikuvat ja selkeät strategiset tavoitteet luovat vakaan pohjan projektioppimisen kehittämiselle.