Evaluation of an eLearning pilot course based on user experiences: corporate case study

Diplomityön tavoitteena oli arvioida sähköisen oppimisen soveltuvuutta kohdeyrityksessä ja selvittää, voidaanko luokkahuonekoulutusta korvata sähköisen oppimisen kursseilla. Tietojärjestelmän raportoinnista tehtiin sähköisen oppimisen kurssi, joka oli koekäytössä. Koekäytön jälkeen tehtiin käyttäjäkysely, kerättiin käyttötietoja kurssista ja tehtiin haastatteluja. Koekäyttäjien kokemuksista tehdyn arvioinnin perusteella sähköinen oppiminen soveltuu käytettäväksi selkeiden asioiden koulutukseen kohdeyrityksessä, mutta se ei voi kokonaan korvata luokkahuonekoulutusta. Luokkahuonekoulutuksessa tulisi keskittyä monimutkaisempiin asioihin ja ongelmanratkaisuun. Positiivisten tulosten perusteella sähköisen oppimisen kehittämistä päätettiin jatkaa yrityksessä. Sähköisen oppimisen kurssin avulla saadaan kustannussäästöjä kohdeyrityksessä, kun käyttäjämäärä on suurempi kuin 66. Jos koko koekäytössä olleen kurssin kohdeyleisö suorittaa kurssin sähköisesti, ovat kustannukset vain noin 15% vastaavista kustannuksista luokkahuoneessa järjestettynä. Lisäksi sähköisen oppimisen tehokkuutta tutkittiin ja koekäytössä olleen kurssin arvioitiin olevan positiivinen työssä kehitetyn Consensus-mallin mukaan.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Clinical Learning Environment and Supervision. Development and Validation of the CLES Evaluation Scale

The purpose of the study is: (1) to describe how nursing students' experienced their clinical learning environment and the supervision given by staff nurses working in hospital settings; and (2) to develop and test an evaluation scale of Clinical Learning Environment and Supervision (CLES). The study has been carried out in different phases. The pilot study (n=163) explored the association between the characteristics of a ward and its evaluation as a learning environment by students. The second version of research instrument (which was developed by the results of this pilot study) were tested by an expert panel (n=9 nurse teachers) and test-retest group formed by student nurses (n=38). After this evaluative phase, the CLES was formed as the basic research instrument for this study and it was tested with the Finnish main sample (n=416). In this phase, a concurrent validity instrument (Dunn & Burnett 1995) was used to confirm the validation process of CLES. The international comparative study was made by comparing the Finnish main sample with a British sample (n=142). The international comparative study was necessary for two reasons. In the instrument developing process, there is a need to test the new instrument in some other nursing culture. Other reason for comparative international study is the reflecting the impact of open employment markets in the European Union (EU) on the need to evaluate and to integrate EU health care educational systems. The results showed that the individualised supervision system is the most used supervision model and the supervisory relationship with personal mentor is the most meaningful single element of supervision evaluated by nursing students. The ward atmosphere and the management style of ward manager are the most important environmental factors of the clinical ward. The study integrates two theoretical elements - learning environment and supervision - in developing a preliminary theoretical model. The comparative international study showed that, Finnish students were more satisfied and evaluated their clinical placements and supervision with higher scores than students in the United Kingdom (UK). The difference between groups was statistical highly significant (p= 0.000). In the UK, clinical placements were longer but students met their nurse teachers less frequently than students in Finland. Arrangements for supervision were similar. This research process has produced the evaluation scale (CLES), which can be used in research and quality assessments of clinical learning environment and supervision in Finland and in the UK. CLES consists of 27 items and it is sub-divided into five sub-dimensions. Cronbach's alpha coefficient varied from high 0.94 to marginal 0.73. CLES is a compact evaluation scale and user-friendliness makes it suitable for continuing evaluation.

Evaluation of an e-learning course: coercion practices in psychiatric nursing

The goal of the study was to evaluate an e-learning course entitled “Nursing interventions to manage distressed and disturbed patients” and intended for psychiatric nurses, using Kirkpatrick’s evaluation model. The aim was to describe nurses’ reactions, learning, behaviour change and impacts resulting from this e-learning course. This dissertation comprises four papers, and the data were collected 2008-2012 from three different sources; electronic databases, an e-learning platform and psychiatric hospitals. First, a systematic literature review was conducted to understand the effectiveness of e-learning. Second, an RCT study was implemented to investigate the impact of the e-learning course on nurses’ job-satisfaction, knowledge and attitudes (N=158). Third, to complete the picture of nurses views of the e-learning course related to knowledge transfer, the nurses’ perspective was studied (N=33). Lastly, the effects of the e-learning course from nursing managers’ perspective in psychiatric hospital organisations were studied (N=28). The systematic review showed that although the nurses were satisfied with the e-learning, no effects were found in the RCT study of nurses’ job satisfaction. The RCT study showed no effects on nurses’ learning related to knowledge increase, but there was change in attitudes. The managers described the changes in the nurses’ knowledge and attitudes. Among the nurses behaviour changed with knowledge transfer from the e-learning course to practice and they pointed out development issues related to their work. The final impacts of the e-learning course revealed advantages and disadvantages of the e-learning course and its implications for nurses’ work. This dissertation provides new insight into nurses’ reactions, learning, behaviour change and impacts resulting from an e-learning course in their continuing education. In order to improve nurses’ continuing education systematic evaluation is needed, for which Kirkpatrick’s evaluation model is a useful tool.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Project Success through Effective Decisions: Case Studies on Project Goal Setting, Success Evaluation and Managerial Decision Making

The objective of the thesis is to structure and model the factors that contribute to and can be used in evaluating project success. The purpose of this thesis is to enhance the understanding of three research topics. The goal setting process, success evaluation and decision-making process are studied in the context of a project, business unitand its business environment. To achieve the objective three research questionsare posed. These are 1) how to set measurable project goals, 2) how to evaluateproject success and 3) how to affect project success with managerial decisions.The main theoretical contribution comes from deriving a synthesis of these research topics which have mostly been discussed apart from each other in prior research. The research strategy of the study has features from at least the constructive, nomothetical, and decision-oriented research approaches. This strategy guides the theoretical and empirical part of the study. Relevant concepts and a framework are composed on the basis of the prior research contributions within the problem area. A literature review is used to derive constructs of factors withinthe framework. They are related to project goal setting, success evaluation, and decision making. On the basis of this, the case study method is applied to complement the framework. The empirical data includes one product development program, three construction projects, as well as one organization development, hardware/software, and marketing project in their contexts. In two of the case studiesthe analytic hierarchy process is used to formulate a hierarchical model that returns a numerical evaluation of the degree of project success. It has its origin in the solution idea which in turn has its foundation in the notion of projectsuccess. The achieved results are condensed in the form of a process model thatintegrates project goal setting, success evaluation and decision making. The process of project goal setting is analysed as a part of an open system that includes a project, the business unit and its competitive environment. Four main constructs of factors are suggested. First, the project characteristics and requirements are clarified. The second and the third construct comprise the components of client/market segment attractiveness and sources of competitive advantage. Together they determine the competitive position of a business unit. Fourth, the relevant goals and the situation of a business unit are clarified to stress their contribution to the project goals. Empirical evidence is gained on the exploitation of increased knowledge and on the reaction to changes in the business environment during a project to ensure project success. The relevance of a successful project to a company or a business unit tends to increase the higher the reference level of project goals is set. However, normal performance or sometimes performance below this normal level is intentionally accepted. Success measures make project success quantifiable. There are result-oriented, process-oriented and resource-oriented success measures. The study also links result measurements to enablers that portray the key processes. The success measures can be classified into success domains determining the areas on which success is assessed. Empiricalevidence is gained on six success domains: strategy, project implementation, product, stakeholder relationships, learning situation and company functions. However, some project goals, like safety, can be assessed using success measures that belong to two success domains. For example a safety index is used for assessing occupational safety during a project, which is related to project implementation. Product safety requirements, in turn, are connected to the product characteristics and thus to the product-related success domain. Strategic success measures can be used to weave the project phases together. Empirical evidence on their static nature is gained. In order-oriented projects the project phases are oftencontractually divided into different suppliers or contractors. A project from the supplier's perspective can represent only a part of the ¿whole project¿ viewed from the client's perspective. Therefore static success measures are mostly used within the contractually agreed project scope and duration. Proof is also acquired on the dynamic use of operational success measures. They help to focus on the key issues during each project phase. Furthermore, it is shown that the original success domains and success measures, their weights and target values can change dynamically. New success measures can replace the old ones to correspond better with the emphasis of the particular project phase. This adjustment concentrates on the key decision milestones. As a conclusion, the study suggests a combination of static and dynamic success measures. Their linkage to an incentive system can make the project management proactive, enable fast feedback and enhancethe motivation of the personnel. It is argued that the sequence of effective decisions is closely linked to the dynamic control of project success. According to the used definition, effective decisions aim at adequate decision quality and decision implementation. The findings support that project managers construct and use a chain of key decision milestones to evaluate and affect success during aproject. These milestones can be seen as a part of the business processes. Different managers prioritise the key decision milestones to a varying degree. Divergent managerial perspectives, power, responsibilities and involvement during a project offer some explanation for this. Finally, the study introduces the use ofHard Gate and Soft Gate decision milestones. The managers may use the former milestones to provide decision support on result measurements and ad hoc critical conditions. In the latter milestones they may make intermediate success evaluation also on the basis of other types of success measures, like process and resource measures.

Evaluation of Teacher Education Programs by Students and Graduates

The purpose of this research was to do a repeated cross-sectional research on class teachers who study in the 4th year and also graduated at the Faculty of Education, University of Turku between the years of 2000 through 2004. Specifically, seven research questions were addressed to target the main purpose of the study: How do class teacher education masters’ degree senior students and graduates rate “importance; effectiveness; and quality” of training they have received at the Faculty of Education? Are there significant differences between overall ratings of importance; effectiveness and quality of training by year of graduation, sex, and age (for graduates) and sex and age (for senior students)? Is there significant relationship between respondents’ overall ratings of importance; effectiveness and their overall ratings of the quality of training and preparation they have received? Are there significant differences between graduates and senior students about importance, effectiveness, and quality of teacher education programs? And what do teachers’ [Graduates] believe about how increasing work experience has changed their opinions of their preservice training? Moreover the following concepts related to the instructional activities were studied: critical thinking skills, communication skills, attention to ethics, curriculum and instruction (planning), role of teacher and teaching knowledge, assessment skills, attention to continuous professional development, subject matters knowledge, knowledge of learning environment, and using educational technology. Researcher also tried to find influence of some moderator variables e.g. year of graduation, sex, and age on the dependent and independent variables. This study consisted of two questionnaires (a structured likert-scale and an open ended questionnaire). The population in study 1 was all senior students and 2000-2004 class teacher education masters’ degree from the departments of Teacher Education Faculty of Education at University of Turku. Of the 1020 students and graduates the researcher was able to find current addresses of 675 of the subjects and of the 675 graduates contacted, 439 or 66.2 percent responded to the survey. The population in study 2 was all class teachers who graduated from Turku University and now work in the few basic schools (59 Schools) in South- West Finland. 257 teachers answered to the open ended web-based questions. SPSS was used to produce standard deviations; Analysis of Variance; Pearson Product Moment Correlation (r); T-test; ANOVA, Bonferroni post-hoc test; and Polynomial Contrast tests meant to analyze linear trend. An alpha level of .05 was used to determine statistical significance. The results of the study showed that: A majority of the respondents (graduates and students) rated the overall importance, effectiveness and quality of the teacher education programs as important, effective and good. Generally speaking there were only a few significant differences between the cohorts and groups related to the background variables (gender, age). The different cohorts were rating the quality of the programs very similarly but some differences between the cohorts were found in the importance and effectiveness ratings. Graduates of 2001 and 2002 rated the importance of the program significantly higher than 2000 graduates. The effectiveness of the programs was rated significantly higher by 2001 and 2003 graduates than other groups. In spite of these individual differences between cohorts there were no linear trends among the year cohorts in any measure. In respondents’ ratings of the effectiveness of teacher education programs there was significant difference between males and females; females rated it higher than males. There were no significant differences between males’ and females’ ratings of the importance and quality of programs. In the ratings there was only one difference between age groups. Older graduates (35 years or older) rated the importance of the teacher training significantly higher that 25-35 years old graduates. In graduates’ ratings there were positive but relatively low correlations between all variables related to importance, effectiveness and quality of Teacher Education Programs. Generally speaking students’ ratings about importance, effectiveness and quality of teacher education program were very positive. There was only one significant difference related to the background variables. Females rated higher the effectiveness of the program. The comparison of students’ and graduates’ perception about importance, effectiveness, and quality of teacher education programs showed that there were no significant differences between graduates and students in the overall ratings. However there were differences in some individual variables. Students rated higher in importance of “Continuous Professional Development”, effectiveness of “Critical Thinking Skills” and “Using Educational Technology” and quality of “Advice received from the advisor”. Graduates rated higher in importance of “Knowledge of Learning Environment” and effectiveness of “Continuous Professional Development”. According to the qualitative data of study 2 some graduates expressed that their perceptions have not changed about the importance, effectiveness, and quality of training that they received during their study time. They pointed out that teacher education programs have provided them the basic theoretical/formal knowledge and some training of practical routines. However, a majority of the teachers seems to have somewhat critical opinions about the teacher education. These teachers were not satisfied with teacher education programs because they argued that the programs failed to meet their practical demands in different everyday situations of the classroom e.g. in coping with students’ learning difficulties, multiprofessional communication with parents and other professional groups (psychologists and social workers), and classroom management problems. Participants also emphasized more practice oriented knowledge of subject matter, evaluation methods and teachers’ rights and responsibilities. Therefore, they (54.1% of participants) suggested that teacher education departments should provide more practice-based courses and programs as well as closer collaboration between regular schools and teacher education departments in order to fill gap between theory and practice.

Evaluation of Implementation of BSc IT Curriculum at Tumaini University

In Tanzania computer knowledge is vital to supplement the pace fast growing economic and development activities, which demands high and reliable level of expertise in com- puting field. In 2006, a research carried out at Tumaini University with purpose to design and implement a contextualized curriculum that can supplement for such needs hence facilitate development in Tanzanian context. A contextualized curriculum took advantage of six principles namely curriculum contex- tualization, projects, practical, interdisciplinary orientation, international recognition and continuous research for the program’s formative and development. Implementation of the curriculum followed the CATI (Contextualize, Apply, Transfer, and Import) model with emphasis on students to identify societal expectations at the early stage in learning process, in which case the graduates will potentially cater for societal expertise needs on ICT. This study adopts an emergent exploratory cross-section research design, while employ- ing a qualitative approach. This study was conducted at Tumaini University in Iringa where by purposeful sampling was used to obtain participants such as students, teach- ers, administrators and employers who participated in several focus group discussions, in-depth interviews and participant observation. The study reveals that six principles are satisfactorily met,despite of bottlenecks such as incompatibility in pedagogical thinking and technology availability for e-learning, learning attitudes, insufficient experts with actual skills and experience,in academic field among the others. The study recommends that iterative longitudinal study should be car- ried out to design for proper intervention in response to these problems which will help in improving and stabilize the curriculum.

Improving Evaluation and Development Capability in Verification and Validation

Validation and verification operations encounter various challenges in product development process. Requirements for increasing the development cycle pace set new requests for component development process. Verification and validation usually represent the largest activities, up to 40 50 % of R&D resources utilized. This research studies validation and verification as part of case company's component development process. The target is to define framework that can be used in improvement of the validation and verification capability evaluation and development in display module development projects. Validation and verification definition and background is studied in this research. Additionally, theories such as project management, system, organisational learning and causality is studied. Framework and key findings of this research are presented. Feedback system according of the framework is defined and implemented to the case company. This research is divided to the theory and empirical parts. Theory part is conducted in literature review. Empirical part is done in case study. Constructive methode and design research methode are used in this research A framework for capability evaluation and development was defined and developed as result of this research. Key findings of this study were that double loop learning approach with validation and verification V+ model enables defining a feedback reporting solution. Additional results, some minor changes in validation and verification process were proposed. There are a few concerns expressed on the results on validity and reliability of this study. The most important one was the selected research method and the selected model itself. The final state can be normative, the researcher may set study results before the actual study and in the initial state, the researcher may describe expectations for the study. Finally reliability of this study, and validity of this work are studied.

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.

Teaching mathematics and programming : new approaches with empirical evaluation

Programming and mathematics are core areas of computer science (CS) and consequently also important parts of CS education. Introductory instruction in these two topics is, however, not without problems. Studies show that CS students find programming difficult to learn and that teaching mathematical topics to CS novices is challenging. One reason for the latter is the disconnection between mathematics and programming found in many CS curricula, which results in students not seeing the relevance of the subject for their studies. In addition, reports indicate that students' mathematical capability and maturity levels are dropping. The challenges faced when teaching mathematics and programming at CS departments can also be traced back to gaps in students' prior education. In Finland the high school curriculum does not include CS as a subject; instead, focus is on learning to use the computer and its applications as tools. Similarly, many of the mathematics courses emphasize application of formulas, while logic, formalisms and proofs, which are important in CS, are avoided. Consequently, high school graduates are not well prepared for studies in CS. Motivated by these challenges, the goal of the present work is to describe new approaches to teaching mathematics and programming aimed at addressing these issues: Structured derivations is a logic-based approach to teaching mathematics, where formalisms and justifications are made explicit. The aim is to help students become better at communicating their reasoning using mathematical language and logical notation at the same time as they become more confident with formalisms. The Python programming language was originally designed with education in mind, and has a simple syntax compared to many other popular languages. The aim of using it in instruction is to address algorithms and their implementation in a way that allows focus to be put on learning algorithmic thinking and programming instead of on learning a complex syntax. Invariant based programming is a diagrammatic approach to developing programs that are correct by construction. The approach is based on elementary propositional and predicate logic, and makes explicit the underlying mathematical foundations of programming. The aim is also to show how mathematics in general, and logic in particular, can be used to create better programs.

Integrating Student Learning into the Repository Ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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.