Syftet med denna studie är att bidra med kunskap om lärares syn på sitt arbete med läsundervisning i årskurs 1. Syftet besvaras genom frågeställningarna: Hur uppger lärarna att de genomför läsundervisning? Vilka läsinlärningsmetoder uppger lärarna att de använder i undervisningen? Hur beskriver lärarna sin betydelse för elevers läsinlärning? Studien har en sociokulturell ansats och speglar således Vygotskijs tankar om lärande och utveckling. Undersökningsmetoden är kvalitativ och bygger på semistrukturerade intervjuer med fyra lärare för närvarande verksamma i årskurs 1. Resultatet visar att lärarna kombinerar flera metoder i läsundervisningen med syfte att eleverna ska få både fonologisk lästräning och helordsträning. Undervisningen har inslag av bokstavsgenomgångar, samtal, läsning, skrivning och på olika sätt arbete med utgångspunkt från en läsebok. Resultatet visar även att lärare behöver vara kunniga och positiva förebilder som motiverar eleverna till läsning. Lärare ska även kunna ge elever utmaningar efter var de befinner sig i sin läsutveckling för att ge alla elever rätt förutsättningar att utvecklas. / The aim of this study is to contribute with knowledge about teachers’ views on their work with literacy learning. The research questions are: How do the teachers describe that they conduct reading lessons? What literacy learning methods do these teachers use in class? How do the teachers describe their own importance as a teacher for the literacy learning of the pupils? This study is based on a socio-cultural perspective on learning and reflects the ideas of Vygotskij on learning and development. Qualitative semi-structured interviews were conducted with four teachers currently active in grade 1. The results show that the teachers combine several different methods in their literacy instruction with the purpose to give the pupils both phonological reading practice and whole words trainings. The instructions have elements such as learning the alphabet, conversations, reading, writing and, in various ways, work based on a textbook. The results show that the teachers need to be knowledgeable and positive role models in order to motivate their pupils to read. Furthermore, the teachers must be able to provide their pupils with challenges adapted to their reading competence in order to give all pupils the right conditions for development.

Context: The main purpose of this thesis is to build an automatic handwritten digit recognition method for the recognition of connected handwritten digit strings. To accomplish the recognition task, first, the digits were segmented into individual digits. Then, a digit recognition module is employed to classify each segmented digit completing the handwritten digit string recognition task. In this study, different machine learning methods, which are SVM, ANN and CNN architectures are used to achieve high performance on the digit string recognition problem. In these methods, images of digit strings are trained with the SVM, ANN and CNN model with HOG feature vectors and Deep learning methods structure by sliding a fixed size window through the images labeling each sub-image as a part of a digit or not. After the completion of the segmentation, to achieve the complete recognition of handwritten digits.Objective: The main purpose of this thesis is to find out the recognition performance of the methods. In order to analyze the performance of the methods, data is needed to be used for training using machine learning methods. Then digit data is tested on the desired machine learning technique. In this thesis, the following methods are performed: Implementation of HOG Feature extraction method with SVM Implementation of HOG Feature extraction method with ANN Implementation of Deep Learning methods with CNN Methods: This research will be carried out using two methods. The first research method is the ¨Literature Review¨ and the second ¨Experiment¨. Initially, a literature review is conducted to get a clear knowledge on the algorithms and techniques which will be used to answer the first research question i.e., to know which type of data is required for the machine learning methods and the data analysis is performed. Later on, with the knowledge of RQ1, Experimentation is conducted to answer the RQ2, RQ3, RQ4. Quantitative data is used to perform the experimentation because qualitative data which obtains from case-study and survey cannot be used for this experiment method as it contains non-numerical data. In this research, an experiment is conducted to find the best suitable machine learning method from the existing methods. As mentioned above in the objectives, an experiment is conducted using SVM, ANN, and CNN. By considering the results obtained from the experiment, a comparison is made on the metrics considered which results in CNN as the best method suitable for Documents Images. Results: Compare the results for SVM, ANN with HOG Feature extraction and the CNN method by using segmented results. Based on the Experiment results it is found that SVM and ANN have some drawbacks like low accuracy and low performance in the recognition of documented images. So, the other method i.e., CNN has greater performance with high accuracy. The following are the results of the recognition rates of each method. SVM performance - 39% ANN performance - 37% CNN performance - 71%. Conclusion: This research concentrates on providing an efficient method for recognition of automatic handwritten digits recognition. Here a sample training data is treated with existing machine learning and deep learning methods like SVM, ANN, and CNN. By the results obtained from the experimentation, it clearly is shown that the CNN method is much efficient with 71% performance when compared to ANN and SVM methods. Keywords: Handwritten Digit Recognition, Handwritten Digit Segmentation, Handwritten Digit Classification, Machine Learning Methods, Deep Learning, Image processing on document images, Support Vector Machine, Conventional Neural Networks, Artificial Neural Networks

A presente pesquisa tem como base a formação continuada de professores do Ensino Médio, que ministram as disciplinas pertencentes à área de Ciências da Natureza, tendo como objetivo aplicar uma metodologia didática que busca o desenvolvimento de habilidades dos alunos, denominada nessa pesquisa de \"Aula Operatória\", e analisar seu efeito na prática pedagógica de professores de Ciências da Natureza. Dessa forma, a partir do ajuste das metodologias utilizadas pelos professores, as aulas possam ser mais motivadoras, interessantes e envolventes, promovendo a melhoria do ensino. As bases teóricas utilizadas para essa pesquisa foram de Cleide do Amaral Terzi e Paulo Afonso Caruso Ronca - que fundamentam as bases do entendimento a respeito de Aula Operatória -, e Philippe Perrenoud, esclarecendo a respeito das competências dos professores, necessárias nos dias de hoje, e fundamentando a parametrização utilizada pelo pesquisador, no processo avaliativo que teve enfoque qualitativo. Buscando as raízes conceituais e práticas da Aula Operatória no decorrer da história, a pesquisa traz os fundamentos do trabalho didático com foco no desenvolvimento de habilidades dos discentes, apresentando John Dewey e as bases da operacionalização do conhecimento, dentro da sua visão instrumentalista; Jean William Fritz Piaget e a teoria desenvolvimentista; Lev Semenovitch Vygotsky e a concepção da aprendizagem mediada; Louis Raths e a importância do \"pensar\". Considerando o problema do baixo índice de aprendizagem e de interesse dos alunos, nas disciplinas pertencentes à área de Ciências da Natureza no cenário da educação no Brasil e no mundo, esta pesquisa propõe a Aula Operatória como prática na formação continuada de professores para a mudança no ensino, visando ao desenvolvimento das habilidades dos alunos. Os professores das disciplinas foram escolhidos de forma voluntária, e a formação para a Aula Operatória se deu nas seguintes etapas: análise diagnóstica realizada a partir de observações qualificadas individuais para planejamento das ações; encontros formativos para oportunizar aos professores a reflexão sobre a própria prática, e para repertoriálos teoricamente com as bases para o desenvolvimento de Aulas Operatórias; execução de Aula Compartilhada como estratégia formativa prática; avaliação final com entrevista e relatório da coordenadora pedagógica em cada caso. Os resultados, de modo geral, mostraram resistência inicial à aplicação da nova metodologia, eliminada com o apoio fornecido pelo pesquisador na elaboração e planejamento das atividades. Posteriormente, foi possível verificar mudança na abordagem dos temas em sala de aula, por parte dos professores, ao empregar os princípios da Aula Operatória, mas mantiveram o registro de planejamento sem mudanças significativas. Desta forma, conclui-se pela necessidade de acompanhamento contínuo para que não haja a retomada de hábitos antigos. / The present research is based on the continuing education of high school teachers, who teach the subjects belonging to the area of Natural Sciences, aiming to apply a teaching methodology which seeks the development of abilities of students, named in this research of \"Operative Classroom\" and analyze your effect on pedagogical practice of teachers of natural sciences. In this way, from the set of methodologies used by teachers, classes may become more motivating, engaging and interesting, promoting the improvement of education. The theoretical bases used for this research were Cleide do Amaral Terzi and Paulo Afonso Caruso Ronca - that underlie the basis of understanding regarding Operative Classroom -, and Philippe Perrenoud, clarifying the competences of teachers, needed these days, and basing the parameterization used by the researcher, in the evaluation process that had a qualitative approach. Seeking practical and conceptual roots of the Operative Classroom in the course of the story, the research brings the basics of didactic work with focus on developing skills of students, featuring John Dewey and the bases of the operationalization of the knowledge within its instrumentalist vision; Jean William Fritz Piaget and theory development; Lev Semenovitch Vygotsky and mediated learning design; Louis Raths and the importance of \"thinking. Considering the problem of the low content of learning and interest of students, in the subjects belonging to the area of natural sciences in education in Brazil and in the world, this research proposes the Operative Classroom practice on continuing education of teachers for change in education, aiming at the development of the skills of the students. Teachers of disciplines were chosen voluntarily, and training for the Operative Classroom took the following steps: diagnostic analysis undertaken from qualified individual observations for planning of actions; formative meetings to provide opportunities for teachers to reflect on their own practice, and for the repertoriár in theory with the bases for the development of Operative Classroom; executing Shared Lessons as formative strategy practice; final review with interview and report the pedagogical coordinator in each case. The results generally showed initial resistance to the application of the new methodology, eliminated with the support provided by the researcher in the development and planning activities. Later, it was possible to verify change in approach the topics in the classroom, on the part of teachers, to employ the principles of Operative Classroom, but they held the record of planning without significant changes. Thus, it is concluded by the need for ongoing monitoring to prevent the resumption of old habits.

The diploma thesis is focused on technical excursions in Sedlčany and their implementation in education at the first level of elementary schools. The theoretical part describes the learning and its life meaning, including the development of the child's thinking with the theory of learning process. It also deals with the technique in the life of contemporary person and his influence on the development of the personality of the child. It focuses on the place of work education at the first level and its aims. It deals with teaching methods, their classification and provides an overview of some activating teaching methods. It defines the excursion and analyzes it didactically. It deals with primary technical litteracy. It characterizes selected technical monuments and sites for excursion. The practical part consists of six suggestions of technical excursions, which can be realized in the teaching of pupils at the first level of elementary schools. The part is the evaluated questionnaire on the effectiveness of the excursions between the first grade teachers. In addition, more general recommendations for applying original excursions in the primary education.

The power industry has undergone significant restructuring throughout the world since the 1990s. In particular, its traditional, vertically monopolistic structures have been reformed into competitive markets in pursuit of increased efficiency in electricity production and utilization. However, along with market deregulation, power systems presently face severe challenges. One is power system stability, a problem that has attracted widespread concern because of severe blackouts experienced in the USA, the UK, Italy, and other countries. Another is that electricity market operation warrants more effective planning, management, and direction techniques due to the ever expanding large-scale interconnection of power grids. Moreover, many exterior constraints, such as environmental protection influences and associated government regulations, now need to be taken into consideration. All these have made existing challenges even more complex. One consequence is that more advanced power system data analysis methods are required in the deregulated, market-oriented environment. At the same time, the computational power of modern computers and the application of databases have facilitated the effective employment of new data analysis techniques. In this thesis, the reported research is directed at developing computational intelligence based techniques to solve several power system problems that emerge in deregulated electricity markets. Four major contributions are included in the thesis: a newly proposed quantum-inspired particle swarm optimization and self-adaptive learning scheme for radial basis function neural networks; online wavelet denoising techniques; electricity regional reference price forecasting methods in the electricity market; and power system security assessment approaches for deregulated markets, including fault analysis, voltage profile prediction under contingencies, and machine learning based load shedding scheme for voltage stability enhancement. Evolutionary algorithms (EAs) inspired by biological evolution mechanisms have had great success in power system stability analysis and operation planning. Here, a new quantum-inspired particle swarm optimization (QPSO) is proposed. Its inspiration stems from quantum computation theory, whose mechanism is totally different from those of original EAs. The benchmark data sets and economic load dispatch research results show that the QPSO improves on other versions of evolutionary algorithms in terms of both speed and accuracy. Compared to the original PSO, it greatly enhances the searching ability and efficiently manages system constraints. Then, fuzzy C-means (FCM) and QPSO are applied to train radial basis function (RBF) neural networks with the capacity to auto-configure the network structures and obtain the model parameters. The benchmark data sets test results suggest that the proposed training algorithms ensure good performance on data clustering, also improve training and generalization capabilities of RBF neural networks. Wavelet analysis has been widely used in signal estimation, classification, and compression. Denoising with traditional wavelet transforms always exhibits visual artefacts because of translation-variant. Furthermore, in most cases, wavelet denoising of real-time signals is actualized via offline processing which limits the efficacy of such real-time applications. In the present context, an online wavelet denoising method using a moving window technique is proposed. Problems that may occur in real-time wavelet denoising, such as border distortion and pseudo-Gibbs phenomena, are effectively solved by using window extension and window circle spinning methods. This provides an effective data pre-processing technique for the online application of other data analysis approaches. In a competitive electricity market, price forecasting is one of the essential functions required of a generation company and the system operator. It provides critical information for building up effective risk management plans by market participants, especially those companies that generate and retail electrical power. Here, an RBF neural network is adopted as a predictor of the electricity market regional reference price in the Australian national electricity market (NEM). Furthermore, the wavelet denoising technique is adopted to pre-process the historical price data. The promising network prediction performance with respect to price data demonstrates the efficiency of the proposed method, with real-time wavelet denoising making feasible the online application of the proposed price prediction method. Along with market deregulation, power system security assessment has attracted great concern from both academic and industry analysts, especially after several devastating blackouts in the USA, the UK, and Russia. This thesis goes on to propose an efficient composite method for cascading failure prevention comprising three major stages. Firstly, a hybrid method based on principal component analysis (PCA) and specific statistic measures is used to detect system faults. Secondly, the RBF neural network is then used for power network bus voltage profile prediction. Tests are carried out by means of the “N-1” and “N-1-1” methods applied in the New England power system through PSS/E dynamic simulations. Results show that system faults can be reliably detected and voltage profiles can be correctly predicted. In contrast to traditional methods involving phase calculation, this technique uses raw data from time domains and is computationally inexpensive in terms of both memory and speed for practical applications. This establishes a connection between power system fault analysis and cascading analysis. Finally, a multi-stage model predictive control (MPC) based load shedding scheme for ensuring power system voltage stability is proposed. It has been demonstrated that optimal action in the process of load shedding for voltage stability during emergencies can be achieved as a consequence. Based on above discussions, a framework for analysing power system voltage stability and ensuring its enhancement is proposed, with such a framework able to be used as an effective means of cascading failure analysis. In summary, the research reported in this thesis provides a composite framework for power system data analysis in a market environment. It covers advanced techniques of computational intelligence and machine learning, also proposes effective solutions for both the market operation and the system stability related problems facing today’s power industry.

This study centres on learning to read and methods for teaching reading skills. The aim is to provide a survey of the concept reading skills and the methods developed on the basis of different theories and the methods preferred when teaching children in primary school and why. The study is based on interviews with eight teachers who are working on a daily basis teaching Swedish in preschool class or lower primary school. The result of my study is that the majority of teachers do not use a specific method, but rather combine several different methods, thus adapting instruction to each individual pupil.

No description available.

The current political dispensation in South Africa has, as was the case in the past, undoubtedly had a major influence on the language patterns of the country. The 1996 Constitution now provides official recognition of the main indigenous languages. Despite this entrenchment, there is evidence that English is seen as the vehicle to the future. This study focuses on the factors that may influence the learning and teaching of Afrikaans as a second additional language in black schools in the Potchefstroom district. In order to achieve this task, a triangulation approach was used. A literature study was done to provide prior information to understanding the current language situation. Interviews were conducted with the respondents as well as the teachers of Afrikaans and the classes were observed and recorded. Questionnaires followed which were completed by the learners as well as their teachers. The objectives of the empirical study were to determine which factors might influence the teaching and learning of Afrikaans as an additional language for both the learners and the teachers, as well as to determine what the implications of such findings for the teaching and learning of Afrikaans as an additional language are. The findings of this study confirm the influence of several factors (socio-political, socio cultural and individual) on Afrikaans. The results indicated that there is a positive attitude towards Afrikaans and that the learners are eager to learn Afrikaans. What is clear, is that it is imperative to take note of these factors to guide the learners towards self regulated study, especially Afrikaans as an additional language. The results also revealed that the education of the teachers need to be addressed to prevent irrevocable damage to Afrikaans and to the relationships between the diverse cultures in our country. / Thesis (M.Ed.)--North-West University, Potchefstroom Campus, 2006.

The current political dispensation in South Africa has, as was the case in the past, undoubtedly had a major influence on the language patterns of the country. The 1996 Constitution now provides official recognition of the main indigenous languages. Despite this entrenchment, there is evidence that English is seen as the vehicle to the future. This study focuses on the factors that may influence the learning and teaching of Afrikaans as a second additional language in black schools in the Potchefstroom district. In order to achieve this task, a triangulation approach was used. A literature study was done to provide prior information to understanding the current language situation. Interviews were conducted with the respondents as well as the teachers of Afrikaans and the classes were observed and recorded. Questionnaires followed which were completed by the learners as well as their teachers. The objectives of the empirical study were to determine which factors might influence the teaching and learning of Afrikaans as an additional language for both the learners and the teachers, as well as to determine what the implications of such findings for the teaching and learning of Afrikaans as an additional language are. The findings of this study confirm the influence of several factors (socio-political, socio cultural and individual) on Afrikaans. The results indicated that there is a positive attitude towards Afrikaans and that the learners are eager to learn Afrikaans. What is clear, is that it is imperative to take note of these factors to guide the learners towards self regulated study, especially Afrikaans as an additional language. The results also revealed that the education of the teachers need to be addressed to prevent irrevocable damage to Afrikaans and to the relationships between the diverse cultures in our country. / Thesis (M.Ed.)--North-West University, Potchefstroom Campus, 2006.

The power industry has undergone significant restructuring throughout the world since the 1990s. In particular, its traditional, vertically monopolistic structures have been reformed into competitive markets in pursuit of increased efficiency in electricity production and utilization. However, along with market deregulation, power systems presently face severe challenges. One is power system stability, a problem that has attracted widespread concern because of severe blackouts experienced in the USA, the UK, Italy, and other countries. Another is that electricity market operation warrants more effective planning, management, and direction techniques due to the ever expanding large-scale interconnection of power grids. Moreover, many exterior constraints, such as environmental protection influences and associated government regulations, now need to be taken into consideration. All these have made existing challenges even more complex. One consequence is that more advanced power system data analysis methods are required in the deregulated, market-oriented environment. At the same time, the computational power of modern computers and the application of databases have facilitated the effective employment of new data analysis techniques. In this thesis, the reported research is directed at developing computational intelligence based techniques to solve several power system problems that emerge in deregulated electricity markets. Four major contributions are included in the thesis: a newly proposed quantum-inspired particle swarm optimization and self-adaptive learning scheme for radial basis function neural networks; online wavelet denoising techniques; electricity regional reference price forecasting methods in the electricity market; and power system security assessment approaches for deregulated markets, including fault analysis, voltage profile prediction under contingencies, and machine learning based load shedding scheme for voltage stability enhancement. Evolutionary algorithms (EAs) inspired by biological evolution mechanisms have had great success in power system stability analysis and operation planning. Here, a new quantum-inspired particle swarm optimization (QPSO) is proposed. Its inspiration stems from quantum computation theory, whose mechanism is totally different from those of original EAs. The benchmark data sets and economic load dispatch research results show that the QPSO improves on other versions of evolutionary algorithms in terms of both speed and accuracy. Compared to the original PSO, it greatly enhances the searching ability and efficiently manages system constraints. Then, fuzzy C-means (FCM) and QPSO are applied to train radial basis function (RBF) neural networks with the capacity to auto-configure the network structures and obtain the model parameters. The benchmark data sets test results suggest that the proposed training algorithms ensure good performance on data clustering, also improve training and generalization capabilities of RBF neural networks. Wavelet analysis has been widely used in signal estimation, classification, and compression. Denoising with traditional wavelet transforms always exhibits visual artefacts because of translation-variant. Furthermore, in most cases, wavelet denoising of real-time signals is actualized via offline processing which limits the efficacy of such real-time applications. In the present context, an online wavelet denoising method using a moving window technique is proposed. Problems that may occur in real-time wavelet denoising, such as border distortion and pseudo-Gibbs phenomena, are effectively solved by using window extension and window circle spinning methods. This provides an effective data pre-processing technique for the online application of other data analysis approaches. In a competitive electricity market, price forecasting is one of the essential functions required of a generation company and the system operator. It provides critical information for building up effective risk management plans by market participants, especially those companies that generate and retail electrical power. Here, an RBF neural network is adopted as a predictor of the electricity market regional reference price in the Australian national electricity market (NEM). Furthermore, the wavelet denoising technique is adopted to pre-process the historical price data. The promising network prediction performance with respect to price data demonstrates the efficiency of the proposed method, with real-time wavelet denoising making feasible the online application of the proposed price prediction method. Along with market deregulation, power system security assessment has attracted great concern from both academic and industry analysts, especially after several devastating blackouts in the USA, the UK, and Russia. This thesis goes on to propose an efficient composite method for cascading failure prevention comprising three major stages. Firstly, a hybrid method based on principal component analysis (PCA) and specific statistic measures is used to detect system faults. Secondly, the RBF neural network is then used for power network bus voltage profile prediction. Tests are carried out by means of the “N-1” and “N-1-1” methods applied in the New England power system through PSS/E dynamic simulations. Results show that system faults can be reliably detected and voltage profiles can be correctly predicted. In contrast to traditional methods involving phase calculation, this technique uses raw data from time domains and is computationally inexpensive in terms of both memory and speed for practical applications. This establishes a connection between power system fault analysis and cascading analysis. Finally, a multi-stage model predictive control (MPC) based load shedding scheme for ensuring power system voltage stability is proposed. It has been demonstrated that optimal action in the process of load shedding for voltage stability during emergencies can be achieved as a consequence. Based on above discussions, a framework for analysing power system voltage stability and ensuring its enhancement is proposed, with such a framework able to be used as an effective means of cascading failure analysis. In summary, the research reported in this thesis provides a composite framework for power system data analysis in a market environment. It covers advanced techniques of computational intelligence and machine learning, also proposes effective solutions for both the market operation and the system stability related problems facing today’s power industry.