Ekonomien ammatillisen kehittymisen tarpeet

Tämä pro gradu -tutkielma käsittelee ekonomien ammatillisen kehittymisen tarpeita muuttuvassa maailmassa, jossa ammatillinen erityisosaaminen vanhenee nopeasti. Tutkimuksessa tarkastellaan ekonomikunnassa koettuja ammatillisen kehittymisen tarpeita substanssin ja oppimistapojen näkökulmasta. Tutkimuksen tavoitteena on selvittää, millaisia ammatillisen kehittymisen tarpeita ekonomeilla on ja miten näihin tarpeisiin voisi vastata. Tutkimuksen pyrkimyksenä on selvittää myös, miten Suomen Ekonomiliitto SEFE voisi auttaa jäseniään kehittymään edelleen ammatillisesti. Aikuisoppimis- ja motivaatioteorioita on olemassa lukuisia. Pro gradun teoriaosassa selvitetään, miten aikuisten oppiminen tapahtuu, mitä se pitää sisällään ja mikä motivoi oppimaan. Lisäksi tutkimuksessa tarkastellaan työelämän ekonomeille asettamia vaatimuksia tänään ja tulevaisuudessa. Pro graduni tutkimusmenetelmänä käytettiin sähköistä kyselyä, ja otoksena oli 2000 SEFEn jäsenrekisteristä poimittua ekonomia. Tutkimustulokset osoittavat, että heterogeenisen ekonomikunnan näkemykset ammatillisen kehittymisen tarpeista ja varsinkin juuri itselle sopivista koulutusmuodoista eroavat melko paljon. Tärkeimmiksi ekonomiosaamisen osa-alueiksi nousivat seikat, joista on hyötyä muuttuvan maailman mukana pysymisessä, kuten valmius omaksua uusia asioita ja ongelmanratkaisutaito. Seuraavaksi tärkeimmäksi osaamisalueeksi nimettiin yleinen talouden tuntemus. Kehittää tulisi paitsi näitä osa-alueita, myös erilaisia johtamistaitoja. Tulosten mukaan suurin este ammatilliselle lisäkoulutukselle on ajan puute. Kyselyn vastauksissa painotettiin koulutuksesta saatavaa hyötyä suhteessa siihen laitettuihin panostuksiin. Kiireessä priorisoidaan työssä oppimista ja lyhyitä täsmäkoulutuksia.

Verkkosovellus oppimistyylien selvittämiseen ja tilastointiin

Oppimistyyleillä määritellään opiskelijan mieltymykset tavoissa, joilla hän vastaanottaa ja omaksuu helpoiten uutta tietoa. Opiskelijan henkilökohtaiseen oppimistyyliin vaikuttavat opiskelijan luonteenpiirteet ja ominaisuudet. Uusien asioiden oppiminen on helpompaa, jos opettajan käyttämä opetustyyli on ainakin osittain yhteneväinen opiskelijan oppimistyylin kanssa. Tässä diplomityössä kehitetty verkkosovellus on tarkoitettu opiskelijoiden käyttöön heidän oppimistyyliensä selvittämiseksi. Opiskelijat rekisteröityvät sovelluksen käyttäjiksi ja antavat samalla itsestään taustatietoja. Tämän jälkeen opiskelijat tekevät sovelluksessa oppimistyylit selvittävän testin. Taustatiedot ja testitulokset tallennetaan tietokantaan. Testituloksen ja oppimistyyleistä tarjolla olevien lisätietojen avulla opiskelijat voivat kehittää omia opiskelutapojaan. Testi on mahdollista tehdä myöhemmin uudelleen, ja tällöin opiskelijat näkevät omassa oppimistyylissään tapahtuneen kehityksen. Opettajat voivat käyttää sovellusta opiskelijoiden testituloksista muodostettavien tilastojen seuraamiseen. Näin opettajilla on mahdollisuus nähdä mitä oppimistyyliä heidän pitämilleen kursseille osallistuvat opiskelijat edustavat. Tämä tieto auttaa opettajia opetussuunnitelman teossa. Sovelluksella voidaan myös muodostaa opiskelijoiden taustatietoihin perustuvia tilastoja tutkimustarkoituksia varten.

Patient Seclusion and Restraint Practices in Psychiatric Hospitals - Towards Evidence Based Clinical Nursing

The overall goal of this study was to support evidence based clinical nursing regarding patient seclusion and restraint practices. This was done by ensuring professional competence through innovative learning methods. The data were collected in three phases between March 2007 and May 2009 on acute psychiatric wards. Firstly, psychiatric inpatients’ experiences and suggestions for seclusion and restraint practices were explored (n=30). Secondly, nursing and medical personnel’s perceptions of seclusion and restraint practices were explored (n=27). Thirdly, the impacts of a continuing vocational eLearning course on nurses’ professional competence was evaluated (n=158). Patients’ perspectives received insufficient attention during the seclusion and restraint process. Improvements and alternatives to seclusion and restraint as suggested by the patients focused on essential parts of clinical nursing, but were not extensively adopted. Also nursing and medical personnel thought that patients’ subjective perspective received little attention. Personnel proposed a number of alternatives to seclusion and restraint, and they expressed a need for education and support to adopt these in clinical nursing. Evaluation of impacts of eLearning course on nurses’ professional competence showed no statistical differences between an eLearning group and an education-as-usual group. This dissertation provides evidence based knowledge about the realization of seclusion and restraint practices and the impacts of eLearning course on nurses’ professional competence in psychiatric hospitals. In order to improve clinical nursing the patient perspective must be accentuated. To ensure personnel’s professional competence, there is a need for written clinical guidelines, education and support. Continuing vocational education should bring together written clinical guidelines, ethical and legal issues and the support for personnel. To achieve the ambitious goal of such integration, achievable and affordable educational programmes are required. This, in turn, yields a call for innovative learning methods.

Kirkkoherrojen osaamisen johtamisen kehittäminen. Case: Lappeenrannan seurakuntayhtymä

Tässä pro gradu -tutkielmassa tarkastellaan osaamisen johtamista Lappeenrannan seurakuntayhtymässä kirkkoherrojen näkökulmasta. Tutkimuksen tavoitteena on selvittää, miten kirkkoherrojen osaamisen johtamista voidaan kehittää. Tutkielmassa tarkastellaan kirkkoherrojen roolia ja tehtäviä sekä käytössä olevia osaamisen kehittämisen menetelmiä. Lisäksi paneudutaan osaamisen johtamisen haasteisiin ja hengellisen työn erityispiirteisiin. Tutkimus toteutettiin kvalitatiivisena tapaustutkimuksena. Tutkimuksen empiirinen aineisto kerättiin haastattelemalla Lappeenrannan seurakuntayhtymän kaikkia viittä kirkkoherraa. Tutkimuksen tulosten perusteella voidaan havaita, että osaamisen johtaminen ei seurakuntayhtymässä ole kovin suunnitelmallista tai pitkäjänteistä. Tulevaisuuden haasteina nähdään etenkin kirkon yhteiskunnallisen aseman muuttuminen ja jäsenmäärän väheneminen. Suurimpana osaamisen johtamiseen liittyvänä haasteena kirkkoherrat kokevat ajan puutteen. Kirkkoherrojen näkemyksissä omasta roolistaan osaamisen johtamisessa korostuvat kokonaisuuksien hallinta, yleisten suuntaviivojen määrittely ja yhteisen suunnan selkiyttäminen. Osaamisen kehittämisen menetelmiä on käytössä monia, mutta pääpaino on keskusteluissa ja palavereissa sekä koulutuksissa. Hengellisen työn erityispiirteinä nähdään kirkon erityinen arvomaailma sekä uskon henkilökohtainen ja intiimi olemus. Osaaminen tulisi seurakuntayhtymässä ottaa tietoiseksi johtamisen kohteeksi. Kirkkoherrat voivat kehittää omaa osaamisen johtamistaan parantamalla tietoisuutta esimiehen eri rooleista ja tehtäväkentistä. Erityisesti yksilöiden oppimisen tukemiseen ja oppimista edistävän ilmapiirin luomiseen tulisi tulevaisuudessa kiinnittää huomiota. Osaamisen kehittämisen menetelmistä suositeltavia ovat etenkin erilaiset työssä oppimisen keinot.

Kranaatinheitinkouluttajien AHJO-ammunnanhallintajärjestelmän osaamisen rakentuminen

Tämä tutkimus osallistuu organisaation oppimiseen liittyvästä osaamisen jalkauttamisesta käytävään akateemiseen ja käytännön johtamisen keskusteluun. Tutkimuksen tavoitteena on kuvata ja ymmärtää, miten kranaatinheitinkouluttajien AHJO-ammunnanhallintajärjestelmän käytön osaaminen rakentuu Puolustus-voimissa. Tutkimuskysymystä lähestytään analysoimalla viittä asiantuntijahaastat-telua ja 58 kirjallista tutkimuskyselyvastausta. Tutkimuksen mukaan AHJO-osaaminen rakentuu yksilötasolla formaalien, non-formaalien ja informaalien oppimismenetelmien vuorovaikutuksessa. Peruskoulutuksen aikainen formaali opetus vaikuttaa korostuvan perusosaamisen rakentamisessa, kun taas syvemmän osaamisen kehittämisen edellytyksenä vaikuttaa olevan informaali harjaantuminen. Non-formaalin täydennyskoulutuksen merkitys korostuu vähemmän harjaantuneen henkilöstön osaamisen rakentamisessa sekä järjestelmän käyttöönottokynnyksen madaltamisessa.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Flipped classroom technology from teachers’ perspective: implementation experience, barriers and perspectives

Blended learning approaches rise their popularity, however not all professors apply them and find them useful and appropriate. This research focuses on study of flipped classroom arrangement and effectiveness of this concept implementation. The Master’s Thesis explores impact of flipped classroom implementation on resource savings for proffesors. The research is based on the literature review of different education arrangements and results of their implementation, on the survey conducted among proffesors from different Universities and on two experiments of flipped classroom implementation. The results reveal advantages and disadvantages of the concept, professors’ attitude to it and possibility to future research and practice in this field

Translation – The fifth language skill? A comparison of the role of translation in Finnish lukio and the International Baccalaureate Diploma Programme

This study attempts to answer the question “Should translation be considered a fifth language skill?” by examining and comparing the use of translation as a language learning and assessment method in the national Finnish lukio curriculum and the curriculum of the International Baccalaureate Diploma Programme (IBDP). Furthermore, the students’ ability to translate and their opinions on the usefulness of translation in language learning will be examined. The students’ opinions were gathered through a questionnaire that was given to 156 students studying in either lukio or the IBDP in Turku and Rovaniemi. I present and compare the role of translation in selected language teaching and learning methods and approaches, and discuss the effectiveness of translation as a language learning method and an assessment method. The theoretical discussion provides the basis for examining the role of translation as a language learning method and an assessment method in the curricula and final examinations of both education programs. The analysis of the two curricula indicated that there is a significant difference in the use of translation, as translation is used as a language learning method and as an assessment method in lukio, but is not used in either form in the IB. The data obtained through the questionnaire indicated that there is a difference in the level of language competence between the lukio and IB students and suggested that the curriculum in which the student studies has some effect on his/her cognitive use of translation, ability to translate and opinions concerning the usefulness of translation in language learning. The results indicated that both groups of students used translation, along with their mother tongue, as a cognitive language learning method, and, contrary to the expectations set by the analysis of the two curricula, the IB students performed better in the translation exercises than lukio students. Both groups of students agreed that translation is a useful language learning method, and indicated that the most common dictionaries they use are bilingual Internet dictionaries. The results suggest that translation is a specific skill that requires teaching and practice, and that perhaps the translation exercises used in lukio should be developed from translating individual words and phrases to translating cultural elements. In addition, the results suggest that perhaps the IB curriculum should include the use of translation exercises (e.g., communicative translation exercises) in order to help students learn to mediate between languages and cultures rather than learn languages in isolation from each other.

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.

Predicting diffusion of innovations with self-organisation and machine learning

The main subject of this master's thesis was predicting diffusion of innovations. The prediction was done in a special case: product has been available in some countries, and based on its diffusion in those countries the prediction is done for other countries. The prediction was based on finding similar countries with Self-Organizing Map~(SOM), using parameters of countries. Parameters included various economical and social key figures. SOM was optimised for different products using two different methods: (a) by adding diffusion information of products to the country parameters, and (b) by weighting the country parameters based on their importance for the diffusion of different products. A novel method using Differential Evolution (DE) was developed to solve the latter, highly non-linear optimisation problem. Results were fairly good. The prediction method seems to be on a solid theoretical foundation. The results based on country data were good. Instead, optimisation for different products did not generally offer clear benefit, but in some cases the improvement was clearly noticeable. The weights found for the parameters of the countries with the developed SOM optimisation method were interesting, and most of them could be explained by properties of the products.

Inter organizational learning within and by innovation networks

The thesis deals with the phenomenon of learning between organizations in innovation networks that develop new products, services or processes. Inter organizational learning is studied especially at the level of the network. The role of the network can be seen as twofold: either the network is a context for inter organizational learning, if the learner is something else than the network (organization, group, individual), or the network itself is the learner. Innovations are regarded as a primary source of competitiveness and renewal in organizations. Networking has become increasingly common particularly because of the possibility to extend the resource base of the organization through partnerships and to concentrate on core competencies. Especially in innovation activities, networks provide the possibility to answer the complex needs of the customers faster and to share the costs and risks of the development work. Networked innovation activities are often organized in practice as distributed virtual teams, either within one organization or as cross organizational co operation. The role of technology is considered in the research mainly as an enabling tool for collaboration and learning. Learning has been recognized as one important collaborative process in networks or as a motivation for networking. It is even more important in the innovation context as an enabler of renewal, since the essence of the innovation process is creating new knowledge, processes, products and services. The thesis aims at providing enhanced understanding about the inter organizational learning phenomenon in and by innovation networks, especially concentrating on the network level. The perspectives used in the research are the theoretical viewpoints and concepts, challenges, and solutions for learning. The methods used in the study are literature reviews and empirical research carried out with semi structured interviews analyzed with qualitative content analysis. The empirical research concentrates on two different areas, firstly on the theoretical approaches to learning that are relevant to innovation networks, secondly on learning in virtual innovation teams. As a result, the research identifies insights and implications for learning in innovation networks from several viewpoints on organizational learning. Using multiple perspectives allows drawing a many sided picture of the learning phenomenon that is valuable because of the versatility and complexity of situations and challenges of learning in the context of innovation and networks. The research results also show some of the challenges of learning and possible solutions for supporting especially network level learning.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Coaching agile methods in research and development projects

Agile coaching of a project team is one way to aid learning of the agile methods. The objective of this thesis is to present the agile coaching plan and to follow how complying the plan affects to the project teams. Furthermore, the agile methods are followed how they work in the projects. Two projects are used to help the research. From the thesis point of view, the task for the first project is to coach the project team and two new coaches. The task for the second project is also to coach the project team, but this time so that one of the new coaches acts as the coach. The agile methods Scrum process and Extreme programming are utilized by the projects. In the latter, the test driven development, continuous integration and pair programming are concentrated more precisely. The results of the work are based on the observations from the projects and the analysis derived from the observations. The results are divided to the effects of the coaching and to functionality of the agile methods in the projects. Because of the small sample set, the results are directional. The presented plan, to coach the agile methods, needs developing, but the results of the functionality of the agile methods are encouraging.

Promoting Programming Learning. Engagement, Automatic Assessment with Immediate Feedback in Visualizations

The skill of programming is a key asset for every computer science student. Many studies have shown that this is a hard skill to learn and the outcomes of programming courses have often been substandard. Thus, a range of methods and tools have been developed to assist students’ learning processes. One of the biggest fields in computer science education is the use of visualizations as a learning aid and many visualization based tools have been developed to aid the learning process during last few decades. Studies conducted in this thesis focus on two different visualizationbased tools TRAKLA2 and ViLLE. This thesis includes results from multiple empirical studies about what kind of effects the introduction and usage of these tools have on students’ opinions and performance, and what kind of implications there are from a teacher’s point of view. The results from studies in this thesis show that students preferred to do web-based exercises, and felt that those exercises contributed to their learning. The usage of the tool motivated students to work harder during their course, which was shown in overall course performance and drop-out statistics. We have also shown that visualization-based tools can be used to enhance the learning process, and one of the key factors is the higher and active level of engagement (see. Engagement Taxonomy by Naps et al., 2002). The automatic grading accompanied with immediate feedback helps students to overcome obstacles during the learning process, and to grasp the key element in the learning task. These kinds of tools can help us to cope with the fact that many programming courses are overcrowded with limited teaching resources. These tools allows us to tackle this problem by utilizing automatic assessment in exercises that are most suitable to be done in the web (like tracing and simulation) since its supports students’ independent learning regardless of time and place. In summary, we can use our course’s resources more efficiently to increase the quality of the learning experience of the students and the teaching experience of the teacher, and even increase performance of the students. There are also methodological results from this thesis which contribute to developing insight into the conduct of empirical evaluations of new tools or techniques. When we evaluate a new tool, especially one accompanied with visualization, we need to give a proper introduction to it and to the graphical notation used by tool. The standard procedure should also include capturing the screen with audio to confirm that the participants of the experiment are doing what they are supposed to do. By taken such measures in the study of the learning impact of visualization support for learning, we can avoid drawing false conclusion from our experiments. As computer science educators, we face two important challenges. Firstly, we need to start to deliver the message in our own institution and all over the world about the new – scientifically proven – innovations in teaching like TRAKLA2 and ViLLE. Secondly, we have the relevant experience of conducting teaching related experiment, and thus we can support our colleagues to learn essential know-how of the research based improvement of their teaching. This change can transform academic teaching into publications and by utilizing this approach we can significantly increase the adoption of the new tools and techniques, and overall increase the knowledge of best-practices. In future, we need to combine our forces and tackle these universal and common problems together by creating multi-national and multiinstitutional research projects. We need to create a community and a platform in which we can share these best practices and at the same time conduct multi-national research projects easily.