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The usage of digital tools for learning in today’s classroom is increasing in pace with our technological developments in our digitized society. This has created several digital language learning platforms that are now available for students and teachers to use for learning. It can be difficult to determine the quality of any learning material and especially new material. In this study, I will examine how well the computer-assisted language learning (CALL) material Duolingo functions as a learning tool for upper-secondary language learning. The purpose of choosing Duolingo was to investigate what qualities modern language learning tools have and to stress the importance of evaluating materials before using them. This was done by evaluating Duolingo in three steps, in an external, internal, and overall process. The evaluating process determines the structure, content and, function of Duolingo as a CALL-material. In the evaluation, pedagogical principles and the English syllabus were used to investigate if Duolingo could be used for educational purposes. The research questions for this study are how appropriate Duolingo is as an educational resource for English upper-secondary school learning and how well it meets the directive of promoting digital tools for knowledge acquisition, communication, and learning. The overall result shows that Duolingo could function as supplementary material but not as the main material since it does not cover the communicative aspects of language learning and works solely with rule-based competence. While Duolingo meets syllabus directives with respect to knowledge acquisition and learning if used for working on grammar and form, it lacks communicative elements and therefore does not support the broad communicative goals of the English subject in upper-secondary school.

This essay presents aspects of fan fiction conducive to language learning, and therefore relevant to teachers of English in upper-secondary schools in Sweden. Students may lack motivation to engage in creative writing assignments in formal learning settings. However, young people engage in fan fiction-writing, and by doing so, develop their language and literacy skills. Drawing upon aspects of fan fiction-writing and participation in fan fiction communities, such as, imitation, interaction, and literary discussions, teachers could find inspiration in these activities to use in their teaching.

Thesis (Ed.D.)--Boston University

Machine learning models automatically learn from historical data to predict unseen events. Such events are often represented as complex multi-dimensional structures. In many cases there is high uncertainty in the prediction process. Research has developed probabilistic models to capture distributions of complex objects, but their learning objective is often agnostic of the evaluation loss. In this thesis, we address the aforementioned defficiency by designing probabilistic methods for structured object prediction that take into account the task at hand. First, we consider that the task at hand is explicitly known, but there is ambiguity in the prediction due to an unobserved (latent) variable. We develop a framework for latent structured output prediction that unifies existing empirical risk minimisation methods. We empirically demonstrate that for large and ambiguous latent spaces, performing prediction by minimising the uncertainty in the latent variable provides more accurate results. Empirical risk minimisation methods predict only a pointwise estimate of the output, however there can be uncertainty on the output value itself. To tackle this deficiency, we introduce a novel type of model to perform probabilistic structured output prediction. Our training objective minimises a dissimilarity coefficient between the data distribution and the model's distribution. This coefficient is defined according to a loss of choice, thereby our objective can be tailored to the task loss. We empirically demonstrate the ability of our model to capture distributions over complex objects. Finally, we tackle a setting where the task loss is implicitly expressed. Specifically, we consider the case of grouped observations. We propose a new model for learning a representation of the data that decomposes according to the semantics behind this grouping, while allowing efficient test-time inference. We experimentally demonstrate that our model learns a disentangled and controllable representation, leverages grouping information when available, and generalises to unseen observations.

Syftet med denna studie är att sammanställa vad tidigare forskning har visat om de utmaningar som finns med att identifiera och bedöma läs- och skrivsvårigheter och/eller dyslexi hos tvåspråkiga elever. Genom en systematisk litteraturstudie undersöks hur läs- och skrivsvårigheter hos tvåspråkiga elever kan identifieras samt vilka faktorer som är viktiga att ta hänsyn till för att göra en tillförlitlig bedömning av läs- och skrivsvårigheter eller dyslexi hos tvåspråkiga elever. I studien granskas och sammanställs sju vetenskapliga artiklar. Ett av resultaten är att tester som undersöker fonologisk medvetenhet på majoritetsspråket, det vill säga på elevernas andraspråk, kan bidra till att identifiera läs- och skrivsvårigheter hos tvåspråkiga elever. Dock visar det sig att dessa tester endast är tillförlitliga om de tvåspråkiga eleverna har uppnått likvärdiga kunskaper i andraspråket som förstaspråkselever. Ytterligare ett resultat är att det finns flera faktorer som är viktiga att ta hänsyn till vid bedömning av läs- och skrivsvårigheter hos tvåspråkiga elever. Det är viktigt att läraren tar hänsyn till elevens hela sociala och språkliga bakgrund, samt hur nära elevens språk ligger varandra med avseende på språkens skriftsystem. När det gäller tester är det viktigt att ta hänsyn till vilka förutsättningar eleven har för att genomföra testet samt hur elevens exponering för det språk som testet genomförs på ser ut.

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Följande studie berör beskrivningar av vilka hinder respektive möjligheter till lärande som förskolegården erbjuder, vilken roll förskollärarna beskriver sig inta samt vilket syfte barns utevistelse på förskolegården har. Vi vill genom studiens resultat bidra till att inspirera förskollärare att tänka kring förhållningssättet gentemot förskolegården samt uppmuntra förskollärare att använda den som ett läranderum. Vi vill uppfylla syftet genom följande frågeställningar: <ul type="disc">Hur påverkas förskollärarnas arbete av förskolegårdens organisation och utformning? Hur beskriver förskollärarna sin roll samt på vilket sätt barns lärande uppmärksammas på förskolegården? Hur beskriver förskollärarna syftet med barns utevistelse på förskolegården?   I studien används den utvecklingspedagogiska teorins synsätt på barns lärande och utveckling som grund. Vi har genomfört en etnografisk studie där vi, genom triangulering, tillämpat flera metoder och fått fram ett resultat som baseras på utbildade förskollärares erfarenheter om lärande på förskolegården, där vi genomfört fyra semistrukturerade djupintervjuer med efterföljande samtalspromenader. I intervjuerna framkom det att lärandet på förskolegården sällan sker vid planerade tillfällen. Samtliga förskollärare upplever sig vara begränsade av på vilket sätt förskolegården utformats och organiserats, vilket påverkar den roll förskolläraren intar, samt bidrar till konsekvenser för barns lärande och utveckling. I studien framkommer att det huvudsakliga syftet med utevistelsen är att barn skall få frisk luft och utöva sin grovmotorik.

Research on machine learning has taken numerous different directions. The present study focussed on the microstructural characteristics of learning systems. It was postulated that learning systems consist of a macrostructure which controls the flow of information, and a micro-structure which manipulates information for decision making. A review of the literature suggested that the basic function of the micro-structure of learning systems was to make a choice among a set of alternatives. This decision function was then equated with the task of making classification decisions. On the basis of the requirements for practical learning systems, the feature frequency approach was chosen for model development. An analysis of the feature frequency approach indicated that an effective model must be sensitive to both within-dimension and between-category variations in frequencies. A model was then developed to provide for such sensitivities. The model was based on the Bayes' Theorem with an assumption of uniform prior probability of occurrence for the categories. This model was tested using data collected for neuropsychological diagnosis of children. Results of the tests showed that the model was capable of learning and provided a satisfactory level of performance. The performance of the model was compared with that of other models designed for the same purpose. The other models included NEXSYS, a rule-based system specially design for this type of diagnosis, discriminant analysis, which is a statistical technique widely used for pattern recognition, and neural networks, which attempt to simulate the neural activities of the brain. Results of the tests showed that the model's performance was comparable to that of the other models. Further analysis indicated that the model has certain advantages in that it has a simple structure, is capable of explaining its decisions, and is more efficient than the other models. / Graduate