1,315 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Teachers’ Professional Competence for Bilingual (Economic) Education

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    Bilingual education holds great potential to simultaneously nurture bilingualism, biliteracy as well as subject-specific and intercultural competences – all crucial skills for the 21st century. However, the widespread implementation of bilingual education faces challenges such as the lack of learning materials and considerable training needs of active bilingual education teachers. Although teachers’ challenges and pedagogical practices within different content-based bilingual education types like immersion, dual-language education or content and language integrated learning are similar, an overarching and comprehensive overview of bilingual education teachers’ required competences is still missing. To address this research gap and ultimately increase the quality of teacher training, the present dissertation closely examines the required competences of bilingual education teachers for secondary education both in general and in the context of the promising bilingual subject of economics. This investigation incorporates a systematic literature review, a mixed-methods study to accumulate practitioners’ insights into professionalism and a linguistic analysis of learning materials. The systematic review encompassed 79 international reports on bilingual education teachers’ competences, which were categorically grouped and narratively synthesised. A competence model specific to bilingual education teachers was developed based on the converging competences found in the competence frameworks and the reports on individual competences. Important competences included several aspects of language proficiency such as subject-specific or academic language proficiency and additional requirements like critical consciousness, cooperation skills, pedagogical/psychological knowledge of methodology or material design and pedagogical content knowledge. The second study used a mixed-methods design with 32 participants (trainee teachers and teacher educators involved in a bilingual education qualification program) filling in a questionnaire and 11 follow-up interviews with participants teaching political studies or geography bilingually. It compared beliefs about generalist and bilingual education teachers’ professional competences and revealed that bilingual education teachers’ competence requirements were more pronounced. These included expanded language proficiency, international content knowledge and pedagogical content knowledge of merging language, content, learning and culture. Higher motivation and enhanced pedagogical/psychological knowledge of material design, methodology and assessment were also deemed important by practitioners. Notable differences between trainee teachers and teacher educators emerged particularly regarding the importance of reflection and the required level of language proficiency. In the third study, the linguistic complexity of 1529 English main body texts in 30 bilingual economics learning materials was analysed. The results showed a lack of systematic complexity progression across grade levels that can potentially hinder students’ continuous language development. Together with substantial fluctuations in lexical richness and the overall scarcity of ready-made materials, these results highlighted the need for bilingual education teachers to create or adapt their own learning materials. To this effect, language proficiency, pedagogical/psychological knowledge of material design and learning processes, (pedagogical) content knowledge and intrinsic motivation were identified as essential for high-quality material production. The present dissertation furthermore discusses and triangulates the results of the three studies to come up with a competence model specifically targeted at bilingual economic education teachers. Overall, it sheds light on teachers’ competences, challenges and opportunities in the field of bilingual (economic) education. Therefore, this comprehensive dissertation contributes to the enhancement of teacher training for bilingual (economic) education. Additionally, the two competence models developed in this dissertation can be used as reflective tools by interested generalist (economic) education teachers. Finally, this dissertation creates a solid foundation for future research, which overall benefits policy, schools, teachers, students and researchers alike

    TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

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    Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag

    Undergraduate Catalog of Studies, 2022-2023

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    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

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    Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method

    Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle

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    Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition

    Accelerating Manufacturing Decisions using Bayesian Optimization: An Optimization and Prediction Perspective

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    Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, man, and machine hours. So, each experiment is valuable and it\u27s critical to determine the optimal experiment location to gain the most comprehensive understanding of the process. Sequential learning is a promising approach to actively learn from the ongoing experiments, iteratively update the underlying optimization routine, and adapt the data collection process on the go. This thesis presents a multi-objective Bayesian optimization framework to find out the optimum processing conditions for a manufacturing setup. It uses an acquisition function to collect data points sequentially and iteratively update its understanding of the underlying design space utilizing a Gaussian Process-based surrogate model. In manufacturing processes, the focus is often on obtaining a rough understanding of the design space using minimal experimentation, rather than finding the optimal parameters. This falls under the category of approximating the underlying function rather than design optimization. This approach can provide material scientists or manufacturing engineers with a comprehensive view of the entire design space, increasing the likelihood of making discoveries or making robust decisions. However, a precise and reliable prediction model is necessary for a good approximation. To meet this requirement, this thesis proposes an epsilon-greedy sequential prediction framework that is distinct from the optimization framework. The data acquisition strategy has been refined to balance exploration and exploitation, and a threshold has been established to determine when to switch between the two. The performance of this proposed optimization and prediction framework is evaluated using real-life datasets against the traditional design of experiments. The proposed frameworks have generated effective optimization and prediction results using fewer experiments
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