[pt] O aprendizado supervisionado é, hoje, o ramo do aprendizado de máquina central para a maioria das inovações nos negócios. A abordagem depende de ter grandes quantidades de dados rotulados, suficiente para ajustar funções com a precisão necessária. No entanto, pode ser caro obter dados rotulados ou criar os rótulos através de um processo de anotação. O aprendizado semisupervisionado (SSL) é usado para rotular com precisão os dados a partir de pequenas quantidades de dados rotulados utilizando técnicas de aprendizado não supervisionado. Uma técnica de rotulagem é a propagação de rótulos. Neste trabalho, usamos especificamente o algoritmo Consensus rate-based label propagation (CRLP). Este algoritmo depende do uma função de consenso para a propagação. Uma possível função de consenso é a matriz de co-associação que estima a probabilidade dos pontos i e j pertencem ao mesmo grupo. Neste trabalho, observamos que a matriz de co-associação contém informações valiosas para tratar esse tipo de problema. Quando nenhum dado está rotulado, é comum escolher aleatoriamente, com probabilidade uniforme, os dados a serem rotulados manualmente, a partir dos quais a propagação procede. Este trabalho aborda o problema de seleção de um conjunto de tamanho fixo de dados para serem rotulados manualmente que propiciem uma melhor precisão no algoritmo de propagação de rótulos. Três técnicas de seleção, baseadas em princípios de amostragem estocástica, são propostas: Stratified Sampling (SS), Probability (P), and Stratified Sampling - Probability (SSP). Eles são todos baseados nas informações embutidas na matriz de co-associação. Os experimentos foram realizados em 15 conjuntos de benchmarks e mostraram resultados muito interessantes. Não só, porque eles fornecem uma seleção mais equilibrada quando comparados a uma seleção aleatória, mas também melhoram os resultados de precisão na propagação de rótulos. Em outro contexto, essas estratégias também foram testadas dentro de um processo de aprendizagem ativa, obtendo também bons resultados. / [en] Supervised learning is, today, the branch of Machine Learning central to most business disruption. The approach relies on having amounts of labeled data large enough to learn functions with the required approximation. However, labeled data may be expensive, to obtain or to construct through a labeling process. Semi-supervised learning (SSL) strives to label accurately data from small amounts of labeled data and the use of unsupervised learning techniques. One labeling technique is label propagation. We use specifically the Consensus rate-based label propagation (CRLP) in this work. A consensus function is central to the propagation. A possible consensus function is a coassociation matrix that estimates the probability of data points i and j belong to the same group. In this work, we observe that the co-association matrix has valuable information embedded in it. When no data is labeled, it is common to choose with a uniform probability randomly, the data to manually label, from which the propagation proceeds. This work addresses the problem of selecting a fixed-size set of data points to label (manually), to improve the label propagation algorithm s accuracy. Three selection techniques, based on stochastic sampling principles, are proposed: Stratified Sampling (SP), Probability (P), and Stratified Sampling - Probability (SSP). They are all based on the information embedded in the co-association matrix. Experiments were carried out on 15 benchmark sets and showed exciting results. Not only because they provide a more balanced selection when compared to a uniform random selection, but also improved the accuracy results of a label propagation method. These strategies were also tested inside an active learning process in a different context, also achieving good results.

No description available.

Active learning has been suggested to be more effective than traditional learning in terms of exam results and time spent on the course material, making it an attractive alternative to the traditional lectures. One way of practicing active learning is through active learnersourcing, a method of learning that also generates material that contributes to further learning. Learnersourcing can be practiced by generating multiple choice questions (MCQs) related to the course material. However, generating useful and high-quality MCQs is challenging for students, especially since the available digital systems developed for this purpose have great issues in terms of user-friendliness and their outdated visual design. Two of these systems are RiPPLE and PeerWise. When developing a platform designed for generating MCQs for learning, there are general and specific guidelines that can be followed: Nielsen’s 10 heuristics for a good user interface/user experience, and 10 principles for a good MCQ. In this thesis, a new system was developed where these guidelines were applied, as an attempt to investigate if the user experience can be improved compared to the currently available interfaces RiPPLE and PeerWise. The project was named MyCleverQuestion. A user test was conducted, in which the three systems’ interfaces for creating and submitting a question were compared and graded. The results shows that the users had the best experience when using MyCleverQuestion. 83.3% of the users said they would use MyCleverQuestion again, stressing the importance of both a good user interface and user experience. / Aktivt lärande har visat sig vara effektivare än traditionellt lärande i avseende till tentamensresultat och tid studenter spenderar på kursmaterialet, och är således ett attraktivt alternativ till de traditionella föreläsningarna. Ett sätt att utöva aktivt lärande är genom att skapa flervalsfrågor kopplade till kursmaterialet. Denna metod kallas aktiv learnersourcing och gör att man genom lärandet även bidrar med material som kan användas för vidare lärande. Det är dock svårt för studenter att skapa högkvalitativa och användbara flervalsfrågor med hjälp av de digitala system som utvecklats för detta syfte, då deras användarvänlighet är bristande och visuella utformning är föråldrade. RiPPLE och PeerWise är två system utvecklade för att skapa flervalsfrågor i utbildningssyfte, och har båda användarvänlighetsproblem. Det finns särskilda riktlinjer som kan följas för att utveckla ett system där studenter kan generera flervalsfrågor för att utöva aktivt lärande. I denna uppsats har både generella och specifika riktlinjer använts: 10 generella heuristiska principer för att skapa en bra användarupplevelse och användargränssnitt, samt 10 principer för att skapa en bra flervalsfrågas, för att slutligen undersöka om användarupplevelsen kan förbättras jämfört med RiPPLE och PeerWise. Namnet som valdes för projektet var MyCleverQuestion. En användarundersökning genomfördes, där gränssnittet för att skapa en fråga för varje system utvärderades. Resultatet visar att gränssnittet med bäst användarupplevelse är MyCleverQuestion. 83,3% av användarna angav att de skulle använda MyCleverQuestion igen, vilket bevisar vikten av ett bra gränssnitt.

Under produktionen av kartong vid det integrerade massa- och kartongbruket, som ägs av Holmen Iggesund Paperboard AB, bildas klorat som är giftigt för vattenlevande organismer. Därför är det väsentligt att utsläppet av klorat till havet, via avloppsvattnet från bruket, hålls under de gränsvärden som fastställts. För att övervaka kloratutsläppet analyseras prover på avloppsvattnet rutinmässigt, och en jonkromatograf ämnades tas i drift för ändamålet. Jonkromatografi är en standardiserad metod för analys av klorat, som måste verifieras innan den tas i bruk. Inga explicita krav fanns på hur omfattande verifieringen skulle vara. Ett syfte med studien var därför att utveckla generella rekommendationer för omfattningen av verifieringen av en standardiserad analysmetod. Litteratur angående verifiering eftersöktes och summerades, och utifrån den togs en verifieringsplan för jonkromatografisk analys av klorat fram. Mätresultat som erhölls utvärderades statistiskt. Slutsatserna var; att verifiering bör planeras på ett sätt så att kalibrering och underhåll av instrumentet sker i samma omfattning som är tänkt vid rutinanvändning av analysmetoden; selektivitet bör testas tidigt, och riktiga prover bör analyseras i ett tidigt skede; omfattningen av verifieringen med avseende på provmatriser och koncentrationsnivåer ska återspegla de provmatriser och koncentrationer som analysmetoden kommer att innefatta i rutinarbetet; antalet försök som bör inkluderas beror på kraven som ställs på de olika egenskaper som definierar prestandan. Minst 6 försök på varje provmaterial var ett riktmärke för precision och riktighet, medan ett försök räckte vid utvärdering av selektivitet och instrumentets mätområde. För att kunna säkerställa god analyskvalité, är utbildning i analysmetodik en viktig del. Ytterligare ett syfte med denna studie var att undersöka utbildningsverksamhetens lärandemål, samt vilka utmaningar som fanns inom utbildningen i analysmetodik. Genom fokusgruppintervjuer och observationer samlades information inom utbildningsverksamheten och dess utmaningar. Utifrån det teoretiska ramverket självstyrd inlärning utfördes en deduktiv tematisk analys av den insamlade datan. Slutsatserna av arbetet löd: Det övergripande syftet med upplärningen inom en analysmetod var att personen som genomgått utbildningen ska kunna ansvara för att tillämpa analysmetoden självständigt utifrån instruktionen, ha kunskap om rimliga mätresultat, kunna reflektera över sitt arbete, samt ha kunskap om syftet med analysmetoden. Ingen standardiserad mall för vad som ska ingå i utbildningen inom en analysmetod fanns, vilket bidrog till osäkerheter angående huruvida likvärdig kompetens uppnåddes för personer under utbildning; dagens utbildningsupplägg inbjöd inte till att personen under upplärning tog på sig ansvaret för uppfyllandet av lärandemålen i den utsträckning som önskades. För att möjliggöra detta föreslogs att ett förberedande moment skulle införas, samt att den lärande skulle få mer tid och möjlighet att genomföra delar av utbildningen självständigt. / During the production of paperboard at the integrated pulp and paperboard mill, owned by Holmen Iggesund Paperboard AB, chlorate is formed, which is a toxic compound for aquatic organisms. It is therefore essential that the chlorate levels in the wastewater from the mill, which is released to the ocean, are below the established limit. In order to monitor the effluent of chlorate, wastewater samples are routinely analyzed, and for this purpose an ion chromatograph was intended to be put to use. Ion chromatography is a standardized method for chlorate analysis, which needs to be verified before being used for routine analyses. No explicit requirements regarding the extent of the verification existed. One purpose of this study was therefore to develop general recommendations regarding the extent of verification of a standardized analytical method. Literature regarding verification was sought for and summarized. Based on the literature, a plan for verifying ion chromatographic analysis of chlorate was constructed. The measurement results obtained were evaluated statistically. Conclusions drawn were: calibration and maintenance of the instrument during the verification process should reflect the frequency of maintenance and calibration planned during the routine use; selectivity should be tested early on, as well as real samples; the extent of the verification with respect to matrices and concentration levels should reflect those that will be included in the routine use of the analytical method; the number of experiments included depends on the requirements imposed on the performance characteristics of the method. At least 6 experiments for each sample was a good aim for verifying precision and trueness, while one experiment was enough for verification of selectivity and the working range of the instrument. In order to ensure satisfactory analysis quality, training in analytical methods is an important aspect. Another purpose of this study was to investigate the learning objectives of the educational activities as well as what challenges could be identified with respect to training in analytical methods. Through focus group interviews and observations, data was collected about the educational activities and challenges. Based on the theoretical framework self-directed learning, a deductive thematic analysis was performed. Conclusions were: The overall purpose of the training in analytical methods was that the person who underwent training should be able to independently apply the analytic method, with the aid of the analytical instruction, have the necessary knowledge about reasonable results, be able to reflect about their work and have knowledge about the purpose of the analytical method. No standardized template regarding what should be included in the training existed, which contributed to uncertainties regarding if equivalent knowledge was achieved for persons under training. The educational approach of today did not encourage the person under training enough to take responsibility for fulfilling the learning objectives. To enable this, it was suggested that a preparation element should be included in the training, and also that the person under training should get more time and opportunities to complete some of the training independently.

The thesis presents the results of an empirical study of emotional intelligence on a sample of psychology students and managers, describes the features of socially-psychological training and seminars as active methods of formation of emotional intelligence. / В диссертации представлены результаты эмпирического исследования эмоционального интеллекта на выборке студентов-психологов и управленцев, описаны особенности социально-психологического тренинга и семинарских занятий как активных методов формирования эмоционального интеллекта.

Structured Product Labels (SPLs), the package inserts that accompany drugs governed by the Food and Drugs Administration (FDA), hold information about Adverse Drug Reactions (ADRs) that exists associated with drugs post-market. This information is valuable for actors working in the field of pharmacovigilance aiming to improve the safety of drugs. One such actor is Uppsala Monitoring Centre (UMC), a non-profit conducting pharmacovigilance research. In order to access the valuable information of the package inserts, UMC have constructed an SPL mining pipeline in order to mine SPLs for ADRs. This project aims to investigate new approaches to the solution to the Scan problem, the part of the pipeline responsible for extracting mentions of ADRs. The Scan problem is solved by approaching the problem as a Named Entity Recognition task, a subtask of Natural Language Processing. By using the transformer-based deep learning model BERT, with domain-specific pre-training, an F1-score of 0.8220 was achieved. Furthermore, the chosen model was used in an iteration of Active Learning in order to efficiently extend the available data pool with the most informative examples. Active Learning improved the F1-score to 0.8337. However, the Active Learning was benchmarked against a data set extended with random examples, showing similar improved scores, therefore this application of Active Learning could not be determined to be effective in this project.

Computing is becoming exponentially more pervasive, and so-called process of ``Digital Transformation'' is but starting. As computers become ever more relevant, our societies will need computing professionals that are well-equipped to face the many challenges their own discipline amplified. The education of computer scientists, so far, mostly focused on equipping them with technical skills. Society and academia, however, are increasingly recognising computing as a field where disciplines collide and intersect. An example that we investigate is that of Innovation and Entrepreneurship (I&E), a field that has often be used to equip computer science students with soft skills and non-technical competences. Computer science faces some unique problems, among which a lower student interest for non-technical subjects, and a constant process of epistemic and technological obsolescence. This thesis showcases some experiences that aim to address these challenges, going towards (re)connecting the Humans and Machines participating in computer science education with the needs of the World of today and tomorrow. Our work combines some theoretical reflections with pedagogical experiments, to ensure that our work has at the same time descriptive power and empirical validation. To aid teachers and learners in the change process, these experiments share a pedagogical approach rooted on Active Learning, ranging from Challenge-Based Learning to Peer Education, to custom-tailored teaching methodologies. In designing each experiment, we start by asking ourselves: how is what we want to teach practiced in the real world? Theoretically, this thesis contributes to the state of the art by conducting a horizontal exploration of how computer science education can enter an age ever more dominated by so-called ambiguity. Methodologically, we propose lightweight techniques for qualitative measurement that are rigorous, but introduce little methodological burden, emphasising our work's reflective and exploratory dimension. Our work aims to show how, using the same broad design process, courses can be flexibly adapted to fit an ever-changing world, including significant disruptions such as the transition to online education.

In the past years, Deep Convolutional Neural Networks have proven to be very useful for 2D Object Detection in many applications. These types of networks require large amounts of labeled data, which can be increasingly costly for companies deploying these detectors in practice if the data quality is lacking. Pool-based Active Learning is an iterative process of collecting subsets of data to be labeled by a human annotator and used for training to optimize performance per labeled image. The detectors used in Active Learning cycles are conventionally pre-trained with a small subset, approximately 2% of available data labeled uniformly at random. This is something I challenged in this thesis by using image metadata. With the motivation of many Machine Learning models being a "jack of all trades, master of none", thus it is hard to train models such that they generalize to all of the data domain, it can be interesting to develop a detector for a certain target metadata domain. A simple Monte Carlo method, Rejection Sampling, can be implemented to sample according to a metadata target domain. This would require a target and proposal metadata distribution. The proposal metadata distribution would be a parametric model in the form of a Gaussian Mixture Model learned from the training metadata. The parametric model for the target distribution could be learned in a similar manner, however from a target dataset. In this way, only the training images with metadata most similar to the target metadata distribution can be sampled. This sampling approach was employed and tested with a 2D Object Detector: Faster-RCNN with ResNet-50 backbone. The Rejection Sampling approach was tested against conventional random uniform sampling and a classical Active Learning baseline: Min Entropy Sampling. The performance was measured and compared on two different target metadata distributions that were inferred from a specific target dataset. With a labeling budget of 2% for each cycle, the max Mean Average Precision at 0.5 Intersection Over Union for the target set each cycle was calculated. My proposed approach has a 40 % relative performance advantage over random uniform sampling for the first cycle, and 10% after 9 cycles. Overall, my approach only required 37 % of the labeled data to beat the next best-tested sampler: the conventional uniform random sampling. / De senaste åren har Djupa Neurala Faltningsnätverk visat sig vara mycket användbara för 2D Objektdetektering i många applikationer. De här typen av nätverk behöver stora mängder av etiketterat data, något som kan innebära ökad kostnad för företag som distribuerar dem, om kvaliteten på etiketterna är bristfällig. Pool-baserad Aktiv Inlärning är en iterativ process som innebär insamling av delmängder data som ska etiketteras av en människa och användas för träning, för att optimera prestanda per etiketterat data. Detektorerna som används i Aktiv Inlärning är konventionellt sätt förtränade med en mindre delmängd data, ungefär 2% av all tillgänglig data, etiketterat enligt slumpen. Det här är något jag utmanade i det här arbetet genom att använda bild metadata. Med motiveringen att många Maskininlärningsmodeller presterar sämre på större datadomäner, eftersom det kan vara svårt att lära detektorer stora datadomäner, kan det vara intressant att utveckla en detektor för ett särskild metadata mål-domän. För att samla in data enligt en metadata måldomän, kan en enkel Monte Carlo metod, Rejection Sampling implementeras. Det skulle behövas en mål-metadata-distribution och en faktisk metadata distribution. den faktiska metadata distributionen skulle vara en parametrisk modell i formen av en Gaussisk blandningsmodell som är tränad på träningsdata. Den parametriska modellen för mål-metadata-distributionen skulle kunna vara tränad på liknande sätt, fast ifrån mål-datasetet. På detta sätt, skulle endast träningsbilder med metadata mest lik mål-datadistributionen kunna samlas in. Den här samplings-metoden utvecklades och testades med en 2D objektdetektor: Faster R-CNN med ResNet-50 bildegenskapextraktor. Rejection sampling metoden blev testad mot konventionell likformig slumpmässig sampling av data och en klassisk Aktiv Inlärnings metod: Minimum Entropi sampling. Prestandan mättes och jämfördes mellan två olika mål-metadatadistributioner som var framtagna från specifika mål-metadataset. Med en etiketteringsbudget på 2%för varje cykel, så beräknades medelvärdesprecisionen om 0.5 snitt över union för mål-datasetet. Min metod har 40%bättre prestanda än slumpmässig likformig insamling i första cykeln, och 10 % efter 9 cykler. Överlag behövde min metod endast 37 % av den etiketterade data för att slå den näst basta samplingsmetoden: slumpmässig likformig insamling.

I takt med att tekniken går framåt krävs det att företag håller sig ständigt uppdaterade genom att vidareutbilda sina medarbetare. Inom företagsutbildning finns behovet av att utveckla metoderna för lärande samt att hitta sätta att utbilda personalen mer effektivt. I tidigare forskning har det framkommit att frågebaserat lärande kan effektivisera utbildningen med 50 %. Den här studien vill undersöka en ny metod som kallas rent frågebaserat lärande, utvecklat av Bälter och Glassey. Rent frågebaserat lärande innebär att lära sig endast genom att svara på frågor och av återkopplingen som följer därefter. Studien som presenteras i detta examensarbete syftar till att utvärdera och undersöka beteendet och erfarenheterna hos deltagare på en kurs som är uppbyggd med metoden rent frågebaserat lärande. Ett existerande utbildningsmaterial om Scanias ledarskapsmodell har först omvandlats med hjälp av den givna metoden och sedan har en grupp medarbetare på Scania testat och utvärderat kursen. Genom enkäter med både kvalitativa och kvantitativa frågor har totalt 27 kursdeltagares upplevelse av sitt eget lärande analyserats med hjälp av tematisk analys och kursmaterialet har utvärderats. Detta har sedan jämförts med resultatet från 15 personer som gick den ursprungliga kursen med individuellt internetbaserat lärande. Det visar sig att gruppen som deltagit i den utvecklade kursen hade mer aktivt lärande jämfört med de som deltagit i den ursprungliga kursen. Kursdeltagarnas uppfattning av hur mycket de har lärt sig skiljer sig inte åt mellan grupperna. Tiden det tog att slutföra kursen har analyserats och av detta framkommer att kursen som är utvecklad med rent frågebaserat lärande kortar ner inlärningstiden med ca 33 %. / As technology advances, companies need to stay updated by continuously training their employees. Therefore, there is a need to make the learning material more efficient and in previous research, question-based learning has been shown to do just that by up to 50%. This study seeks to investigate a new method called pure question-based learning, developed by Bälter and Glassey. Pure question-based learning means learning only by answering questions and the feedback that follows. The study that is presented in this thesis aims to evaluate and investigate the behavior and experience of participants in a course that is structured with the method pure question-based learning. An existing training material on Scania's leadership model has first been transformed using the given method and then a group of employees at Scania has tested and evaluated the course. Through surveys with both qualitative and quantitative questions, 27 course participants' experience of their own learning has been analyzed with the help of thematic analysis and the course material has been evaluated. The result has since then been compared to the results of 15 participants in the original course with individual internet-based learning. It turns out that the group that participated in the developed course had more active learning compared to the group that participated in the original course. The course participants' perception of how much they learned does not differ between the groups. The time it took to complete the course has been analyzed and from this it appears that the course that is developed with pure question-based learning shortens the learning time by approximately 33 %.

The Swedish curriculum emphasizes the importance of pupils’ active learning, influence over the education and communicative skills in language learning. The Storyline approach has since its entry in the educational context in the 1960s, influenced teaching all over the world. The approach emphasizes pupils’ interest, meaning-making processes and creativity and thus agrees well with the content of the Swedish curriculum. This research overview aims to offer insight into how the Storyline approach can affect pupils in an English as a second language context, in different ways. It focuses primarily on how the Storyline approach can promote pupils’ second language acquisition, what impact the story and its characters have on pupils’ motivation and what effects the aesthetics in Storyline have on pupils. It also gives an overview of challenges that pupils and teachers might encounter while working with a Storyline project. The findings of the study indicate that the Storyline approach, through group work and communicative, meaningful tasks, promote pupils’ learning of English. Moreover, the context of the story and the characters seem to enhance pupils’ intrinsic motivation to learn the English language and the aesthetics can make pupil active agents in their learning process and mediate their knowledge. However, the Storyline approach also involves different challenges for both pupils and teachers, such as trying to find a balance between the teacher’s and the pupils’ control, keeping the story and the characters alive throughout the whole project, dealing with lack of time, group work issues and presenting the work in front of other pupils.