Pagrindinis informacinių technologijų (IT) naudojimo mokymuisi tikslas – didinti mokymosi kokybę ir efektyvumą, lengvinti besimokančiojo ir mokytojo darbą. Galima išskirti dvi IT taikymo ugdymui kryptis: 1) kai naudojant IT siekiama gerinti tradicinius metodus 2) kai sukuriami nauji metodai, kuriuos taikyti įmanoma tik naudojant IT. Abiem atvejais svarbus mokytojų gerosios patirties dalijimasis, mokymosi metodų įvaldymas. Disertacinis darbas skirtas metodinių išteklių ir mokymosi metodų naudojimo problemoms, kylančioms dėl informacijos nepakankamumo mokymosi objektų (MO) metaduomenų saugyklose, spręsti. Išanalizuoti ir palyginti pagrindiniai MO metaduomenų standartų modeliai, naudojami skaitmeniniams mokymosi ištekliams formaliuoju būdu aprašyti. Ištirti MO metaduomenų standartų taikymo modelių sudarymo moksliniai ir praktiniai principai. Išanalizuoti turinio MO kūrimo modeliai, kurie užtikrina MO suderinamumą. Atliktas empirinis tyrimas leido nustatyti tolesnę tyrimo kryptį ir turėjo įtakos išplėsto MO metaduomenų taikomojo modelio kūrimui. Metaduomenų taikomojo modelio projektavimo procesą sudaro šie etapai: 1) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų elementų aibių išskyrimas; 2) valdomųjų žodynų, skirtų metaduomenų elementams aprašyti, formavimas siekiant užtikrinti metaduomenų suderinamumą; 3) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų lyginamoji analizė; 4) išplėsto MO metaduomenų modelio išbaigimas ir diegimas... [toliau žr. visą tekstą] / The key aim of using information technology (IT) in learning is to increase the learning quality and efficiency, to facilitate a learner’s and a teacher’s work. We can distinguish two main directions of IT application in training: (1) if applying IT we strive to improve the traditional methods; (2) if new methods are developed that are applicable only if IT is used. In both cases, sharing the good experience of teachers and mastering of the learning methods are of great importance. The research work is meant for solving the problems of using methodological resources and learning methods, which arise due to insufficiency of information in the metadata repositories of learning object (LO). The main models of LO metadata standards, used to describe digital learning resources in a formal way, have been analyzed and compared. Scientific and practical principles for applied model design of LO metadata standards have been explored. The content LO developing models that ensure LO compatibility have been considered. The empirical research performed enabled us to determine a further trend of research and influenced the creation of an extended LO metadata applied model. The design process of a metadata applied model consists of the following phases: 1) determination of sets of metadata elements that describe methodological resources and learning method objects, 2) composition of controlled vocabularies, aimed at description of metadata elements, with a view to ensure the compatibility... [to full text]

The key aim of using information technology (IT) in learning is to increase the learning quality and efficiency, to facilitate a learner’s and a teacher’s work. We can distinguish two main directions of IT application in training: (1) if applying IT we strive to improve the traditional methods; (2) if new methods are developed that are applicable only if IT is used. In both cases, sharing the good experience of teachers and mastering of the learning methods are of great importance. The research work is meant for solving the problems of using methodological resources and learning methods, which arise due to insufficiency of information in the metadata repositories of learning object (LO). The main models of LO metadata standards, used to describe digital learning resources in a formal way, have been analyzed and compared. Scientific and practical principles for applied model design of LO metadata standards have been explored. The content LO developing models that ensure LO compatibility have been considered. The empirical research performed enabled us to determine a further trend of research and influenced the creation of an extended LO metadata applied model. The design process of a metadata applied model consists of the following phases: 1) determination of sets of metadata elements that describe methodological resources and learning method objects, 2) composition of controlled vocabularies, aimed at description of metadata elements, with a view to ensure the compatibility... [to full text] / Pagrindinis informacinių technologijų (IT) naudojimo mokymuisi tikslas – didinti mokymosi kokybę ir efektyvumą, lengvinti besimokančiojo ir mokytojo darbą. Galima išskirti dvi IT taikymo ugdymui kryptis: 1) kai naudojant IT siekiama gerinti tradicinius metodus 2) kai sukuriami nauji metodai, kuriuos taikyti įmanoma tik naudojant IT. Abiem atvejais svarbus mokytojų gerosios patirties dalijimasis, mokymosi metodų įvaldymas. Disertacinis darbas skirtas metodinių išteklių ir mokymosi metodų naudojimo problemoms, kylančioms dėl informacijos nepakankamumo mokymosi objektų (MO) metaduomenų saugyklose, spręsti. Išanalizuoti ir palyginti pagrindiniai MO metaduomenų standartų modeliai, naudojami skaitmeniniams mokymosi ištekliams formaliuoju būdu aprašyti. Ištirti MO metaduomenų standartų taikymo modelių sudarymo moksliniai ir praktiniai principai. Išanalizuoti turinio MO kūrimo modeliai, kurie užtikrina MO suderinamumą. Atliktas empirinis tyrimas leido nustatyti tolesnę tyrimo kryptį ir turėjo įtakos išplėsto MO metaduomenų taikomojo modelio kūrimui. Metaduomenų taikomojo modelio projektavimo procesą sudaro šie etapai: 1) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų elementų aibių išskyrimas; 2) valdomųjų žodynų, skirtų metaduomenų elementams aprašyti, formavimas siekiant užtikrinti metaduomenų suderinamumą; 3) metodinių išteklių ir mokymosi metodų objektų aprašančių metaduomenų lyginamoji analizė; 4) išplėsto MO metaduomenų modelio išbaigimas ir diegimas... [toliau žr. visą tekstą]

Σε αυτή την Διπλωματική εργασία μελετήσαμε με ποιο τρόπο μπορούν να εφαρμοστούν οι διάφορες τεχνικές Εξόρυξης Γνώσης (Data Mining) στην εκπαίδευση. Αυτός ο επιστημονικός τομέας o οποίος ερευνά και αναπτύσσει τεχνικές προκειμένου να ανακαλύψει γνώση από δεδομένα τα οποία προέρχονται από την εκπαίδευση ονομάζεται Εξόρυξη Γνώσης από Εκπαιδευτικά Δεδομένα (Educational Data Mining –EDM. Στην εργασία αυτή εκτός από την θεωρητική μελέτη των αλγορίθμων και των τεχνικών που διέπουν την εξόρυξη γνώσης από δεδομένα γενικά, έγινε και μια λεπτομερέστερη μελέτη και παρουσίαση της κατηγορίας των αλγορίθμων κατηγοριοποίησης (Classification), διότι αυτοί οι αλγόριθμοι χρησιμοποιήθηκαν στην φάση της υλοποίησης/αξιολόγησης. Στην συνέχεια η εργασία επικεντρώθηκε στον τρόπο με τον οποίο μπορούν να εφαρμοστούν αυτοί οι αλγόριθμοι σε εκπαιδευτικά δεδομένα, τι εφαρμογές έχουμε στην εκπαίδευση, ενώ αναφερόμαστε και σε μια πληθώρα ερευνών που έχουν πραγματοποιηθεί πάνω στο συγκεκριμένο αντικείμενο. Στην συνέχεια διερευνήσαμε την εφαρμογή τεχνικών κατηγοριοποίησης στην πρόγνωση της επίδοσης μαθητών Δευτεροβάθμιας Εκπαίδευσης στα μαθήματα της Γεωγραφίας Α’ και Β’ Γυμνασίου. Συγκεκριμένα υλοποιήσαμε και θα αξιολογήσαμε έξι αλγορίθμους οι οποίοι ανήκουν στην ομάδα των αλγορίθμων κατηγοριοποίησης(Classification) και είναι αντιπροσωπευτικοί των σημαντικότερων τεχνικών κατηγοριοποίησης. Από την οικογένεια των ταξινομητών με χρήση δένδρων απόφασης (Decision Tree Classifiers) υλοποιήσαμε τον J48, από τους αλγορίθμους κανόνων ταξινόμησης (Rule-based Classification ) τον Ripper, από τους αλγόριθμους στατιστικής κατηγοριοποίησης τον Naïve Bayes, από την μέθοδο των Κ πλησιέστερων γειτόνων (KNN) τον 3-ΝΝ, από την κατηγορία των τεχνητών νευρωνικών δικτύων τον Back Propagation και τέλος από τις μηχανές διανυσμάτων υποστήριξης (Support Vector Machines SVM) τον SMO (Sequental Minimal Optimazation). Όλες οι παραπάνω υλοποιήσεις και αξιολογήσεις έγιναν με το ελεύθερο λογισμικού Weka το οποίο είναι υλοποιημένο σε Java και το οποίο προσφέρει μια πληθώρα αλγορίθμων μηχανικής μάθησης για να κάνουμε εξόρυξη γνώσης. / In this work we will study the way the misc data mining techniques can be applied to the misc fields of the education. This new scientific field is commonly named Educational Data Mining. In this study we will study the theoretical analysis of the data mining techniques focussing to the classification techniques as those are the most commonly used for prediction purpose. We also intend to predict student performance in secondary education using data mining techniques. The data we collect are concerned the class of Geography and we apply to them six data mining models with the help of the open source machine learning software Weka. We use supervised machine learning algorithms from the Classification field (Decision Tree Classifiers, Rule-based Classification, Neural Networks, k-Nearest Neighbour Algorithm, Bayesian and Support Vector Machines). After we have evaluate the algorithms we build a java tool, that uses the 3-KNN algorithm, to help us predict the performance of a student at the end of the year.

Meaningful learning requires active teaching and learning approaches. Thus, with a specific focus on Mathematics teaching at university in Oramia, the study aimed to: • examine the extent to which active learning/student-centered approaches were implemented; • assess the attitudes of university lecturers towards active-learning; • investigate whether appropriate training and support have been provided for the implementation of an active learning approaches • assess the major challenges that hinder the implementation of active learning approaches and • recommend ways that could advance the use of active learning approaches in Mathematics teaching at university. A mixed-methods design was used. Among the six universities in the Oromia Regional State of Ethiopia, two of the newly established universities (younger than 5 years) and two of the old universities (15 years and older) were involved in the study. A total of 84 lecturers participated in the study and completed questionnaires. This was complemented by a qualitative approach that used observation checklists and interviews for data gathering: 16 lessons were observed while the lecturers taught their mathematics classes (two lecturers from each of the four sample universities were twice observed). In addition, semi-structured interviews were conducted with four mathematics department heads and eight of the observed lecturers. The study adhered to ethical principles and to applied several techniques to enhance the validity/trustworthiness of the findings. / Psychology of Education / D. Ed. (Psychology of Education)

This study investigates the use of co-operative learning in the teaching of mapwork to Geography students in tertiary education. Diverse methods of teaching Geography mapwork and also theories of learning that are relevant to the teaching of mapwork are discussed. Co-operative learning, and how it can be employed in the teaching of mapwork is fully explained. The study revealed that co-operative learning method can help students to achieve higher marks in mapwork. It is an instructional method that uses small groups of students working together to meet educational goals. The approach relies on interaction and interdependence and thus is especially suited to higher level conceptual tasks requiring problem-solving and decision-making. / Psychology of Education / M. Ed. (Psychology of Education)

[pt] Sistemas de computação paralelos vêm se tornando pervasivos, sendo usados para interagir com o mundo físico e processar uma grande quantidade de dados de várias fontes. É essencial, portanto, a melhora contínua do desempenho computacional para acompanhar o ritmo crescente da quantidade de informações que precisam ser processadas. Algumas dessas aplicações admitem uma menor qualidade no resultado final em troca do aumento do desempenho de execução. Este trabalho tem por objetivo avaliar a viabilidade de usar métodos de aprendizagem supervisionada para garantir que a técnica de Sincronização Relaxada, utilizada para o aumento do desempenho de execução, forneça resultados dentro de limites aceitáveis de erro. Para isso, criamos uma metodologia que utiliza alguns dados de entrada para montar casos de testes que, ao serem executados, irão fornecer valores representativos de entrada para o treinamento de métodos de aprendizagem supervisionada. Dessa forma, quando o usuário utilizar a sua aplicação (no mesmo ambiente de treinamento) com uma nova entrada, o algoritmo de classificação treinado irá sugerir o fator de relaxamento de sincronização mais adequado à tripla aplicação/entrada/ambiente de execução. Utilizamos essa metodologia em algumas aplicações paralelas bem conhecidas e mostramos que, aliando a Sincronização Relaxada a métodos de aprendizagem supervisionada, foi possível manter a taxa de erro máximo acordada. Além disso, avaliamos o ganho de desempenho obtido com essa técnica para alguns cenários em cada aplicação. / [en] Parallel computing systems have become pervasive, being used to interact with the physical world and process a large amount of data from various sources. It is essential, therefore, the continuous improvement of computational performance to keep up with the increasing rate of the amount of information that needs to be processed. Some of these applications admit lower quality in the final result in exchange for increased execution performance. This work aims to evaluate the feasibility of using supervised learning methods to ensure that the Relaxed Synchronization technique, used to increase execution performance, provides results within acceptable limits of error. To do so, we have created a methodology that uses some input data to assemble test cases that, when executed, will provide input values for the training of supervised learning methods. This way, when the user uses his/her application (in the same training environment) with a new input, the trained classification algorithm will suggest the relax synchronization factor that is best suited to the triple application/input/execution environment. We used this methodology insome well-known parallel applications and showed that, by combining Relaxed Synchronization with supervised learning methods, it was possible to maintain the maximum established error rate. In addition, we evaluated the performance gain obtained with this technique for a number of scenarios in each application.

This thesis will concentrate on previous and current learning methods of French as a foreign language. This understanding will help to plot the rapidness of evolution within foreign-language teaching. In conjunction with this evolution the thesis will also give a brief overview of the exponential development of technology. It will focus specifically on how technology created a new way of learning. The aim of this thesis is to depict whether there is a need to adapt the French language classroom with the current learning technologies in use. The thesis will also show that since people are different and since each generation differs in its learning preference, that technology can help to bridge the ever-growing gap between the learner and the learning material since people learn work on different ways. According to the proof that generations differ from each other and that the current young generation is referred to as the Net-generation, it will be clearly shown that this generation prefers to learn with technology. The correct mix of learning methods, learning technologies and different learning styles is one that is humanly impossible to achieve in a conventional way. It is on this basis then that the thesis will show that the correct e-learning technology should form an integral part of the new language classroom as it is the only solution to ensure that learning stays current and adaptive, and that it keeps on playing an important part in the evolution of mankind. Furthermore, a brief study will be conducted on the current and prospective use of e-learning technologies in the French language classroom of South Africa. / Thesis (MA (French))--University of Pretoria, 2004. / Modern European Languages / unrestricted

The aim of this master thesis is to explore the process of learning and development of workers in the Czech industrial company dealing with the manufacture of tires. The thesis is divided into a theoretical and a practical part. In the theoretical part the content of methods and processes as well as the principles of staff learning and development are explained. The practical part contains the analysis of the principles and methods used in the education and development of workers in a particular enterprise, focused on the administrative and manual workers. As part of the conducted analysis measures to improve the process of staff education and development are proposed.

<div>In recent years, intelligent devices such as smartphones and self-driving cars are becoming ubiquitous in daily life, and thus, wireless communication is turning out to be increasingly omnipresent. To efficiently utilize the electromagnetic spectrum, automatically reconfigurable software-controlled radio transceivers are drawing an extensive amount of attention. In order to implement a reconfigurable radio transceiver, automatically tunable RF front-end components such as tunable filters are indispensable. Over the last decade, tunable filters have shown promising performance with high-quality factor (Q), a wide tuning range, and high-power handling. However, most of the existing tunable filters are manually adjusted. In this regard, this research work focuses on developing a novel automatic software-driven tuning technique for continuously tunable microwave and millimeter-wave filters.</div><div><br></div><div><br></div><div>First, a K-band continuously tunable bandpass filter has been demonstrated with contactless printed circuit board (PCB) tuners. Then, an automatic tuning technique based on deep-Q learning has been proposed and realized to tune a filter with contactless tuners automatically. Two-pole, three-pole, and four-pole bandpass filters are experimentally tested as examples without any human intervention to prove the feasibility of the tuning technique. For the first time, unlike a look-up table, the filters can be continuously tuned at a practically infinite number of frequencies inside the tuning range. </div><div><br></div><div>Next, a K/Ka-band tunable absorptive bandstop filter (ABSF) has been designed and fabricated in low-cost PCB technology. Contrary to a reflective bandstop filter, an ABSF filter is preferred for interference mitigation due to its deeper notch and lower reflection. However, the absorbed power may limit the filter's power handling. Therefore, lastly, a comparative analysis of power handling capability (PHC) between a reflective bandstop filter and an absorptive bandstop filter has been studied theoretically and experimentally in this dissertation.</div>

<div> <div> <div> <p>First-order methods such as Stochastic Gradient Descent are methods of choice for solving non-convex optimization problems in machine learning. These methods primarily rely on the gradient of the loss function to estimate descent direction. However, they have a number of drawbacks, including converging to saddle points (as opposed to minima), slow convergence, and sensitivity to parameter tuning. In contrast, second order methods that use curvature information in addition to the gradient, have been shown to achieve faster convergence rates, theoretically. When used in the context of machine learning applications, they offer faster (quadratic) convergence, stability to parameter tuning, and robustness to problem conditioning. In spite of these advantages, first order methods are commonly used because of their simplicity of implementation and low per-iteration cost. The need to generate and use curvature information in the form of a dense Hessian matrix makes each iteration of second order methods more expensive. </p><p><br></p> <p>In this work, we address three key problems associated with second order methods – (i) what is the best way to incorporate curvature information into the optimization procedure; (ii) how do we reduce the operation count of each iteration in a second order method, while maintaining its superior convergence property; and (iii) how do we leverage high-performance computing platforms to significant accelerate second order methods. To answer the first question, we propose and validate the use of Fisher information matrices in second order methods to significantly accelerate convergence. The second question is answered through the use of statistical sampling techniques that suitably sample matrices to reduce per-iteration cost without impacting convergence. The third question is addressed through the use of graphics processing units (GPUs) in distributed platforms to deliver state of the art solvers.</p></div></div></div><div><div><div> <p>Through our work, we show that our solvers are capable of significant improvement over state of the art optimization techniques for training machine learning models. We demonstrate improvements in terms of training time (over an order of magnitude in wall-clock time), generalization properties of learned models, and robustness to problem conditioning. </p> </div> </div> </div>