New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

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.

Conditions for EFL learning and professional development : Finland-Swedish learner and teacher perspectives

Engelskans dominerande roll som internationellt språk och andra globaliseringstrender påverkar också Svenskfinland. Dessa trender påverkar i sin tur förutsättningarna för lärande och undervisning i engelska som främmande språk, det vill säga undervisningsmålen, de förväntade elev- och lärarroller, materialens ändamålsenlighet, lärares och elevers initiala erfarenheter av engelska och engelskspråkiga länder. Denna studie undersöker förutsättningarna för lärande och professionell utveckling i det svenskspråkiga nybörjarklassrummet i engelska som främmande språk. Utgångsläget för 351 nybörjare i engelska som främmande språk och 19 av deras lärare beskrivs och analyseras. Resultaten tyder på att engelska håller på att bli ett andraspråk snarare än ett traditionellt främmande språk för många unga elever. Dessa elever har också goda förutsättningar att lära sig engelska utanför skolan. Sådan var dock inte situationen för alla elever, vilket tyder på att det finns en anmärkningsvärd heterogenitet och även regional variation i det finlandssvenska klassrummet i engelska som främmande språk. Lärarresultaten tyder på att vissa lärare har klarat av att på ett konstruktivt sätt att tackla de förutsättningar de möter. Andra lärare uttrycker frustration över sin arbetssituation, läroplanen, undervisningsmaterialen och andra aktörer som kommer är av betydelse för skolmiljön. Studien påvisar att förutsättningarna för lärande och undervisning i engelska som främmande språk varierar i Svenskfinland. För att stöda elevers och lärares utveckling föreslås att dialogen mellan aktörer på olika nivå i samhället bör förbättras och systematiseras.

Viestintä ravitsemusosaamisen kehittämisessä Case:Sairaalan ateriaprosessi

Tutkielman tarkoituksena oli tutkia viestinnän merkitystä osaamisen kehittämisessä. Tavoitteena oli tutkia, miten viestintä edistää ravitsemusosaamisen kehittämistä sairaalan ateriaprosessissa. Tutkimuksessa etsittiin vastausta kysymyksiin, mitkä ovat ravitsemusosaamisen kehittämisen ja viestinnän tavoitteet, millä työyhteisöviestinnän foorumeilla uuden ravitsemushoitosuosituksen ja ravitsemushoidon strategian edellyttämiä muutoksia käsitellään ja millaisia työssä oppimisen prosesseja näillä foorumeilla on tunnistettavissa. Empirian näkökulmasta tutkimusta voidaan kuvata tapaustutkimukseksi. Tapauksena on sairaalan ateriaprosessi. Tutkimuksen valmistelevana aineistona käytettiin uutta ravitsemushoitosuositusta (Nuutinen ym. 2010), jota täydennettiin haastatteluaineistolla. Tutkimuksessa ovat edustettuina hoitotyön, ruokapalvelun ja ravitsemushoidon asiantuntemuksen näkökulmat sairaalasta sekä ammatti- ja aikuisopistosta. Tutkimusmenetelmänä käytettiin teemahaastatteluja. Haastattelut nauhoitettiin ja litteroitiin tekstimuotoon. Aineisto analysoitiin teemakortiston ja teemoittelun avulla. Tutkimuksen tulokset osoittavat, että ravitsemusosaamisen kehittämisen tavoitteena on uuden ravitsemushoitosuosituksen ja ravitsemushoidon strategian edellyttämien muutosten toteuttaminen sairaalan ravitsemushoidon prosesseissa ja tuotteissa. Ravitsemusosaamisen kehittämisen tavoitteena on tässä yhteydessä ateriaprosessin ja ruokapalvelun tuotteiden eli ruokavalioiden kehittäminen. Ravitsemushoidon kehittämisen tarkoituksena on asiakkaiden toipumisen, elämänlaadun ja hyvinvoinnin edistäminen sekä terveydenhuollon kustannusten säästäminen. Viestinnällä on tärkeä merkitys ravitsemusosaamisen kehittämisessä. Viestinnän avulla edistetään yksilöllistä ja yhteistä eli tiimioppimista vuorovaikutuksen kautta. Ruokapalvelu- ja hoitohenkilöstön sekä ravitsemushoidon asiantuntijoiden välinen vuoropuhelu nähdään tärkeänä ravitsemusosaamisen kehittämisessä. Vuoropuhelun avulla vahvistetaan ravitsemushoitoon liittyvää tietopohjaa ja yhteistä käsitteistöä. Tavoitteena on yhteisen kielen ja toimintamallin luominen ravitsemushoidon kehittämiseen. Ravitsemushoitosuosituksen ja ravitsemushoidon strategian edellyttämiä muutoksia käsitellään ulkoisissa ja sisäisissä verkostoissa esimerkiksi ravitsemus-yhdyshenkilöverkoston tapaamisissa, moniammatillisissa työryhmissä, henkilöstö- ja oppisopimuskoulutuksissa sekä työfoorumilla eli fyysisessä työtilassa ja hyödyntäen viestintäteknologiaa. Hoitotyön, ruokapalvelun ja ravitsemushoidon asiantuntijoilla/opettajilla on tärkeä rooli ravitsemusosaamisen kehittämiseen liittyvässä työssä oppimisen ohjaamisessa. Ravitsemusosaamisen kehittämisessä on tunnistettavissa sosiaalisia, reflektiivisiä, kognitiivisia ja operationaalisia työssä oppimisen prosesseja. Sosiaalisia prosesseja ovat työkokemusten vaihdanta ja reflektiivisiä niiden arviointi. Kognitiivisten prosessien tarkoitus on tiedonhankinta ja prosessointi, jolloin yhdistetään kokemustietoa sekä uutta ravitsemustieteellistä tietoa. Tavoitteena on yhteisen kielen ja toimintamallin luominen, jota kokeillaan käytännössä. Operationaalisia prosesseja ovat fyysisessä työtilassa tapahtuva kokeilemalla, tekemällä ja soveltamalla oppiminen, jolloin uutta toimintamallia esimerkiksi vajaaravitsemuksen seulontaa, ateriatilausta tai reseptiikkaa kokeillaan käytännössä. Johtopäätöksenä voidaan todeta, että sairaalassa on omaksuttu oppivan organisaation periaatteita ravitsemusosaamisen kehittämisessä. Ravitsemusosaamisen kehittäminen on yhteydessä muutokseen, strategiaan, prosessien ja tuotteiden kehittämiseen. Viestinnän avulla edistetään ravitsemushoitosuosituksen ja ravitsemushoidon strategian edellyttämien muutosten toteuttamista sairaalan ateriaprosessissa ja ruokavalioissa. Hoito- ja ruokapalveluhenkilöstön sekä ravitsemushoidon asiantuntijoiden välisen vuoropuhelun tavoitteena on yhteisen kielen ja toimintamallin luominen ravitsemushoidon kehittämiseen. Tutkimus palvelee ravitsemusosaamisen kehittämistä sairaalan ateriaprosessissa. Tutkimuksen tuloksia on mahdollista käyttää vertailuoppimismateriaalina terveydenhuollon organisaatioissa ja verkostoissa.

Making Sense of Shared Leadership. A case study of leadership processes and practices without formal leadership structure in the team context

Leadership is essential for the effectiveness of the teams and organizations they are part of. The challenges facing organizations today require an exhaustive review of the strategic role of leadership. In this context, it is necessary to explore new types of leadership capable of providing an effective response to new needs. The presentday situations, characterized by complexity and ambiguity, make it difficult for an external leader to perform all leadership functions successfully. Likewise, knowledge-based work requires providing professional groups with sufficient autonomy to perform leadership functions. This study focuses on shared leadership in the team context. Shared leadership is seen as an emergent team property resulting from the distribution of leadership influence across multiple team members. Shared leadership entails sharing power and influence broadly among the team members rather than centralizing it in the hands of a single individual who acts in the clear role of a leader. By identifying the team itself as a key source of influence, this study points to the relational nature of leadership as a social construct where leadership is seen as social process of relating processes that are co-constructed by several team members. Based on recent theoretical developments concerned with relational, practice-based and constructionist approaches to the study of leadership processes, this thesis proposes the study of leadership interactions, working processes and practices to focus on the construction of direction, alignment and commitment. During the research process, critical events, activities, working processes and practices of a case team have been examined and analyzed with the grounded theory –approach in the terms of shared leadership. There are a variety of components to this complex process and a multitude of factors that may influence the development of shared leadership. The study suggests that the development process of shared leadership is a common sense -making process and consists of four overlapping dimensions (individual, social, structural, and developmental) to work with as a team. For shared leadership to emerge, the members of the team must offer leadership services, and the team as a whole must be willing to rely on leadership by multiple team members. For these individual and collective behaviors to occur, the team members must believe that offering influence to and accepting it from fellow team members are welcome and constructive actions. Leadership emerges when people with differing world views use dialogue and collaborative learning to create spaces where a shared common purpose can be achieved while a diversity of perspectives is preserved and valued. This study also suggests that this process can be supported by different kinds of meaning-making and process tools. Leadership, then, does not reside in a person or in a role, but in the social system. The built framework integrates the different dimensions of shared leadership and describes their relationships. This way, the findings of this study can be seen as a contribution to the understanding of what constitutes essential aspects of shared leadership in the team context that can be of theoretical value in terms of advancing the adoption and development process of shared leadership. In the real world, teams and organizations can create conditions to foster and facilitate the process. We should encourage leaders and team members to approach leadership as a collective effort that the team can be prepared for, so that the response is rapid and efficient.

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.

Learning to Understand the Retailers - The New Approach of a Paperboard Manufacturer

The aim of the study is to expand networking between a packaging material manufacturer and retailers in order to develop products which appeal to brand owners and their customers. The in-built targets are to understand the retailer’s role in the value chain, clarify who makes packaging decision of private label products, and canvass the importance of sustainability. The present value chain of the packaging material manufacturer is reviewed first. It is assumed that sustainability could be a common interest, and The Consumer Goods Forum’s “A Global Language for Packaging and Sustainability” report is shortly discussed. The presentation of the most common packaging materials is based on a guide called “Packaging in the Sustainability Agenda: A Guide for Corporate Decision Makers”. The terms manufacturer’s brand and private label are defined. A retail value chain with emphasis on the role of customers as partners is introduced. The study area is the Nordic countries, and the information about Nordic retailers was provided first by desk research. The interviews were made in Finland, Sweden, Norway and Denmark. The study method is qualitative: the intention was to get initial insights, ideas and understandings. The results are compiled under the headings: sustainability, private labels, cooperation and packaging development. Also the reasons for good profitability of private labels are explained. Sustainability or responsibility is a key driver for innovation in the retail sector. Private labels have become brands. The ways of cooperation between a packaging material manufacturer and a retailer could be education and training. Packaging development is of great interest to retailers and they are willing to contribute.

The nature of communication structures of university students in an international web-based computer-supported collaborative learning (CSCL) course

Communication, the flow of ideas and information between individuals in a social context, is the heart of educational experience. Constructivism and constructivist theories form the foundation for the collaborative learning processes of creating and sharing meaning in online educational contexts. The Learning and Collaboration in Technology-enhanced Contexts (LeCoTec) course comprised of 66 participants drawn from four European universities (Oulu, Turku, Ghent and Ramon Llull). These participants were split into 15 groups with the express aim of learning about computer-supported collaborative learning (CSCL). The Community of Inquiry model (social, cognitive and teaching presences) provided the content and tools for learning and researching the collaborative interactions in this environment. The sampled comments from the collaborative phase were collected and analyzed at chain-level and group-level, with the aim of identifying the various message types that sustained high learning outcomes. Furthermore, the Social Network Analysis helped to view the density of whole group interactions, as well as the popular and active members within the highly collaborating groups. It was observed that long chains occur in groups having high quality outcomes. These chains were also characterized by Social, Interactivity, Administrative and Content comment-types. In addition, high outcomes were realized from the high interactive cases and high-density groups. In low interactive groups, commenting patterned around the one or two central group members. In conclusion, future online environments should support high-order learning and develop greater metacognition and self-regulation. Moreover, such an environment, with a wide variety of problem solving tools, would enhance interactivity.

The good and the successful - Examination of informal learning mechanisms

This thesis strived to find out which informal learning (IL) mechanisms are used the most by the respondents. Additionally, the goal was to know more about the respondents as informal learners and what could explain possible differences. The target was to resolve whether informal learning explains differences in individual performance or, do some other explanations for success exist. Informal learning was to be made more visible, since many are unaware of it. Relevant IL mechanisms that the interviewees could explain were selected for this thesis. The theory on informal learning was presented and some additional informal learning mechanisms were included: Underlying learning theories, internal and external learning resources, as well as some sport related informal learning mechanisms. Various scholars have explained these terms. The final results of this thesis relate to business context, but sport is at the scope of my research. The target group consisted of nine individuals in team sports that were considered as high performers (good/successful). Hence, also the concept of high performance was clarified with competence, expertise and talent literature. The study is qualitative and face-to-face interviews were chosen. The data was analyzed with Grounded Theory principles and theory elaboration. This thesis pointed out similarities and differences between the respondents´ answers (good/successful, inexperienced/experienced). Thus, the analysis clarified that there are different attitudes to learning and different learner profiles in sports context. Also, it became clear that some informal learning mechanisms are more used than others. Secondly, based on the most crucial differences, Typology of Talentum was formulated based on Le Deist & Winteron´s (2005, 39) Typology of holistic competence. Some variables of informal learning seemed to constitute the Meta-competence of Typology that ultimately causes the differences in individual performance and success. The results can be transferred to business context because meta-competence is transferable by nature.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Sentence Compression with Supervised Machine Learning

The subject of the thesis is automatic sentence compression with machine learning, so that the compressed sentences remain both grammatical and retain their essential meaning. There are multiple possible uses for the compression of natural language sentences. In this thesis the focus is generation of television program subtitles, which often are compressed version of the original script of the program. The main part of the thesis consists of machine learning experiments for automatic sentence compression using different approaches to the problem. The machine learning methods used for this work are linear-chain conditional random fields and support vector machines. Also we take a look which automatic text analysis methods provide useful features for the task. The data used for machine learning is supplied by Lingsoft Inc. and consists of subtitles in both compressed an uncompressed form. The models are compared to a baseline system and comparisons are made both automatically and also using human evaluation, because of the potentially subjective nature of the output. The best result is achieved using a CRF - sequence classification using a rich feature set. All text analysis methods help classification and most useful method is morphological analysis. Tutkielman aihe on suomenkielisten lauseiden automaattinen tiivistäminen koneellisesti, niin että lyhennetyt lauseet säilyttävät olennaisen informaationsa ja pysyvät kieliopillisina. Luonnollisen kielen lauseiden tiivistämiselle on monta käyttötarkoitusta, mutta tässä tutkielmassa aihetta lähestytään television ohjelmien tekstittämisen kautta, johon käytännössä kuuluu alkuperäisen tekstin lyhentäminen televisioruudulle paremmin sopivaksi. Tutkielmassa kokeillaan erilaisia koneoppimismenetelmiä tekstin automaatiseen lyhentämiseen ja tarkastellaan miten hyvin erilaiset luonnollisen kielen analyysimenetelmät tuottavat informaatiota, joka auttaa näitä menetelmiä lyhentämään lauseita. Lisäksi tarkastellaan minkälainen lähestymistapa tuottaa parhaan lopputuloksen. Käytetyt koneoppimismenetelmät ovat tukivektorikone ja lineaarisen sekvenssin mallinen CRF. Koneoppimisen tukena käytetään tekstityksiä niiden eri käsittelyvaiheissa, jotka on saatu Lingsoft OY:ltä. Luotuja malleja vertaillaan Lopulta mallien lopputuloksia evaluoidaan automaattisesti ja koska teksti lopputuksena on jossain määrin subjektiivinen myös ihmisarviointiin perustuen. Vertailukohtana toimii kirjallisuudesta poimittu menetelmä. Tutkielman tuloksena paras lopputulos saadaan aikaan käyttäen CRF sekvenssi-luokittelijaa laajalla piirrejoukolla. Kaikki kokeillut teksin analyysimenetelmät auttavat luokittelussa, joista tärkeimmän panoksen antaa morfologinen analyysi.

Language-specific aspects of reading acquisition: the case of Russian

We have investigated Russian children’s reading acquisition during an intermediate period in their development: after literacy onset, but before they have acquired well-developed decoding skills. The results of our study suggest that Russian first graders rely primarily on phonemes and syllables as reading grain-size units. Phonemic awareness seems to have reached the metalinguistic level more rapidly than syllabic awareness after the onset of reading instruction, the reversal which is typical for the initial stages of formal reading instruction creating external demand for phonemic awareness. Another reason might be the inherent instability of syllabic boundaries in Russian. We have shown that body-coda is a more natural representation of subsyllabic structure in Russian than onset-rime. We also found that Russian children displayed variability of syllable onset and offset decisions which can be attributed to the lack of congruence between syllabic and morphemic word division in Russian. We suggest that fuzziness of syllable boundary decisions is a sign of the transitional nature of this stage in the reading development and it indicates progress towards an awareness of morphologically determined closed syllables. Our study also showed that orthographic complexity exerts an influence on reading in Russian from the very start of reading acquisition. Besides, we found that Russian first graders experience fluency difficulties in reading orthographically simple words and nonwords of two and more syllables. The transition from monosyllabic to bisyllabic lexical items constitutes a certain threshold, for which the syllabic structure seemed to be of no difference. When we compared the outcomes of the Russian children with the ones produced by speakers of other languages, we discovered that in the tasks which could be performed with the help of alphabetic recoding Russian children’s accuracy was comparable to that of children learning to read in relatively shallow orthographies. In tasks where this approach works only partially, Russian children demonstrated accuracy results similar to those in deeper orthographies. This pattern of moderate results in accuracy and excellent performance in terms of reaction times is an indication that children apply phonological recoding as their dominant strategy to various reading tasks and are only beginning to develop suitable multiple strategies in dealing with orthographically complex material. The development of these strategies is not completed during Grade 1 and the shift towards diversification of strategies apparently continues in Grade 2.

On a practical realization of Kolb's experiential learning cycle

This study aims to extend prior knowledge on the learning and developmental outcomes of the experiential learning cycle of David Kolb by the analysis of its practical realization at Team Academy. The study is based on the constructivist approach to learning and considers, among others, the concepts of autonomy support, Nonaka and Takeuchi's knowledge creation model, Luft and Ingham's Johari Window and Deci and Ryan's Self-determination theory. For the investigation deep interviews were carried out with the participants of Team Academy, both learners and coaches. Taking the interview results and the above described theories into consideration this study concludes that experiential learning results not only in effective learning, but also in a remarkable soft skill acquisition, self-development and increase in motivation with an internal locus of causality. Real-life projects permit the learners to experience real challenges. By the practical activities and teamwork they also get the possibility to find out their personal strengths, weaknesses and unique capacities.

Fostering product innovation by learning through exporting in Eastern Europe

This study discusses the importance of learning through the process of exporting, and more specifically how such a process can enhance the product innovativeness of a company. The purpose of this study is to investigate the appropriate sources of learning and to suggest an interactive framework for how new knowledge from exporting markets can materialize itself into product innovation. The theoretical background of the study was constructed from academic literature, which is related to concepts of learning by exporting, along with sources for learning in the market and new product development. The empirical research in the form of a qualitative case study was based on four semi-structured interviews and secondary data from the case company official site. The interview data was collected between March and April 2015 from case company employees who directly work in the department of exporting and product development. The method of thematic analysis was used to categorize and interpret the collected data. What was conclusively discovered, was that the knowledge from an exporting market can be an incentive for product innovation, especially an incremental one. Foreign customers and competitors as important sources for new knowledge contribute to the innovative process. Foreign market competitors’ influence on product improvements was high only when the competitor was a market leader or held a colossal market share, while the customers’ influence is always high. Therefore, involving a foreign customer in the development of a new product is vital to a company that is interested in benefiting from what is learned through exporting. The interactive framework, which is based on the theoretical background and findings of the study, suggests that exporting companies can raise their product innovativeness by utilizing newly gained knowledge from exporting markets. Except for input, in the form of sources of learning, and product innovation as an output, the framework contains a process of knowledge transfer, the absorptive capacity of a firm and a new product development process. In addition, the framework and the findings enhance the understanding of the disputed relationship between an exporting experience and product innovation. However, future research is needed in order to fully understand all the elements of the framework, such as the absorptive capacity of a firm as well as more case companies to be processed in order to increase the generalization of the framework