Small-class school is a kind of school where two or more classes are taught together. This type of organization is demanding for teachers, their lesson planning and the whole learning process. Important part of small-class teacher's activity is indirect pedagogical work. It means organization of regular or irregular activities which are necessary for socio-cultural function of those schools in a village. It is possible to label small-class schools as alternative schools where unconventional teaching methods are used. Main goal of this thesis was to find out which types of teaching methods and didactic tools are used most frequently in small-class schools. Other goals of this thesis were to describe a range of indirect pedagogical work and co-operation of small-class schools with special pedagogues.

Second language (L2) researchers and teachers have increasingly come to believe that using a computer-based corpus can be extremely helpful in the language classroom. The purpose of this study is to examine whether corpora can be used outside of the classroom in order for students to improve their essays independently. No previous study has tried to examine students' essays in relation to corpus use so that this study is exploratory. Seven international students wrote five essays on specific topics and then corrected their errors through corpus research. Two experiments were conducted with different students and followed three steps: receiving information about how to use the BYU COCA, writing and correcting, and interviews with students. I examined quantitatively the number and types of errors that students were able to correct in two experiments and reported qualitatively on students' interview responses.

[ES] La Industria 4.0 se enfrenta a importantes retos a la hora de perseguir la transformación digital y la eficiencia operativa. La creciente complejidad de los entornos industriales modernos lleva a la necesidad de desplegar tecnologías digitales y, sobre todo, la automatización de la Industria. Sin embargo, este camino hacia la innovación va acompañado de numerosos obstáculos, ya que el entorno cambia constantemente. Por lo tanto, para adaptarse a esta evolución, es necesario emplear planteamientos más flexibles. Estos planteamientos están estrechamente relacionados con el uso de la AI y RL, ya que surgen como soluciones clave para abordar los retos cruciales de la navegación cooperativa de agentes dentro de entornos dinámicos. Mientras tanto, los algoritmos RL se enfrentan a las complejidades que implica la transmisión y el procesamiento de grandes cantidades de datos, para hacer frente a este desafío, la tecnología 5G emerge como un habilitador clave para las soluciones de escenarios de problemas evolutivos. Entre las principales ventajas de la 5G están que ofrece una transmisión rápida y segura de grandes volúmenes de datos con una latencia mínima. Al ser la única tecnología hasta ahora capaz de ofrecer estas capacidades, 5G se convierte en un componente esencial para desplegar servicios en tiempo real como la navegación cooperativa. Además, otra ventaja es que proporciona la infraestructura necesaria para intercambios de datos robustos y contribuye a la eficiencia del sistema y a la seguridad de los datos en entornos industriales dinámicos. A la vista de lo anterior, es evidente que la complejidad de los entornos industriales conduce a la necesidad de proponer sistemas basados en nuevas tecnologías como las redes AI y 5G, ya que su combinación proporciona una potente sinergia. Además, aparte de abordar los retos identificados en la navegación cooperativa, también abre la puerta a la implementación de fábricas inteligentes, dando lugar a mayores niveles de automatización, seguridad y productividad en las operaciones industriales. Es importante destacar que la aplicación de técnicas de AI conlleva la necesidad de utilizar software de simulación para probar los algoritmos propuestos en entornos virtuales. Esto permite abordar cuestiones esenciales sobre la validez de los algoritmos, reducir los riesgos de daños en el hardware y, sobre todo, optimizar las soluciones propuestas. Con el fin de proporcionar una solución a los retos fundamentales en la automatización de fábricas, esta Tesis se centra en la integración de la robótica móvil en la nube, especialmente en el contexto de la Industria 4.0. También abarca la investigación de las capacidades de las redes 5G, la evaluación de la viabilidad de simuladores como ROS y Gazebo, y la fusión de datos de sensores y el diseño de algoritmos de planificación de trayectorias basados en RL. En otras palabras, esta Tesis no solo identifica y aborda los retos clave de la Industria 4.0, sino que también presenta soluciones innovadoras e hipótesis concretas para la investigación. Además, promueve la combinación de AI y 5G para desplegar servicios en tiempo real, como la navegación cooperativa. Así, aborda retos críticos y demuestra que la colaboración tecnológica redefine la eficiencia y la adaptabilidad en la industria moderna. / [CA] La Indústria 4.0 s'enfronta a importants reptes a l'hora de perseguir la transformació digital i l'eficiència operativa. La creixent complexitat dels entorns industrials moderns porta a la necessitat de desplegar tecnologies digitals i, sobretot, l'automatització de la Indústria. No obstant això, este camí cap a la innovació va acompanyat de nombrosos obstacles, ja que l'entorn canvia constantment. Per tant, per a adaptar-se a esta evolució, és necessari emprar plantejaments més flexibles. Estos plantejaments estan estretament relacionats amb l'ús de l'AI i RL, ja que sorgixen com a solucions clau per a abordar els reptes crucials de la navegació cooperativa d'agents dins d'entorns dinàmics. Mentrestant, els algorismes RL s'enfronten a les complexitats que implica la transmissió i el processament de grans quantitats de dades, per a fer front a este desafiament, la tecnologia 5G emergix com un habilitador clau per a les solucions d'escenaris de problemes evolutius. Entre els principals avantatges de la 5G estan que oferix una transmissió ràpida i segura de grans volums de dades amb una latència mínima. A l'ésser l'única tecnologia fins ara capaç d'oferir estes capacitats, 5G es convertix en un component essencial per a desplegar servicis en temps real com la navegació cooperativa. A més, un altre avantatge és que proporciona la infraestructura necessària per a intercanvis de dades robustes i contribuïx a l'eficiència del sistema i a la seguretat de les dades en entorns industrials dinàmics. A la vista de l'anterior, és evident que la complexitat dels entorns industrials conduïx a la necessitat de proposar sistemes basats en noves tecnologies com les xarxes AI i 5G, ja que la seua combinació proporciona una potent sinergia. A més, a part d'abordar els reptes identificats en la navegació cooperativa, també obri la porta a la implementació de fabriques intel·ligents, donant lloc a majors nivells d'automatització, seguretat i productivitat en les operacions industrials. És important destacar que l'aplicació de tècniques d'AI comporta la necessitat d'utilitzar programari de simulació per a provar els algorismes proposats en entorns virtuals. Això permet abordar qüestions essencials sobre la validesa dels algorismes, reduir els riscos de dona'ns en el maquinari i, sobretot, optimitzar les solucions proposades. Amb la finalitat de proporcionar una solució als reptes fonamentals en l'automatització de fabriques, esta Tesi se centra en la integració de la robòtica mòbil en el núvol, especialment en el context de la Indústria 4.0. També abasta la investigació de les capacitats de les xarxes 5G, l'avaluació de la viabilitat de simuladors com ROS i Gazebo, i la fusió de dades de sensors i el disseny d'algorismes de planificació de trajectòries basats en RL. En altres paraules, esta Tesi no sols identifica i aborda els reptes clau de la Indústria 4.0, sinó que també presenta solucions innovadores i hipòtesis concretes per a la investigació. A més, promou la combinació d'AI i 5G per a desplegar servicis en temps real, com la navegació cooperativa. Així, aborda reptes crítics i demostra que la col·laboració tecnològica redefinix l'eficiència i l'adaptabilitat en la indústria moderna. / [EN] Industry 4.0 faces significant challenges in pursuing digital transformation and operational efficiency. The increasing complexity of modern industrial environments leads to the need to deploy digital technologies and, above all, Industry automation. However, this path to innovation is accompanied by numerous obstacles, as the environment constantly changes. Therefore, to adapt to this evolution, it is necessary to employ more flexible approaches. These approaches are closely linked to the use of Artificial Intelligence (AI) and Reinforcement Learning (RL), as they emerge as pivotal solutions to address the crucial challenges of cooperative agent navigation within dynamic environments. Meanwhile, RL algorithms face the complexities involved in transmitting and processing large amounts of data. To address this challenge, Fifth Generation (5G) technology emerges as a key enabler for evolutionary problem scenario solutions. Among the main advantages of 5G is that it offers fast and secure transmission of large volumes of data with minimal latency. As the only technology so far capable of delivering these capabilities, 5G becomes an essential component for deploying real-time services such as cooperative navigation. Furthermore, another advantage is that it provides the necessary infrastructure for robust data exchanges and contributes to system efficiency and data security in dynamic industrial environments. In view of the above, it is clear that the complexity of industrial environments leads to the need to propose systems based on new technologies such as AI and 5G networks, as their combination provides a powerful synergy. Moreover, aside from tackling the challenges identified in cooperative navigation, it also opens the door to the implementation of smart factories, leading to higher levels of automation, safety, and productivity in industrial operations. It is important to note that the application of AI techniques entails the need to use simulation software to test the proposed algorithms in virtual environments. This makes it possible to address essential questions about the validity of the algorithms, reduce the risks of damage to the hardware, and, above all, optimize the proposed solutions. In order to provide a solution to the fundamental challenges in factory automation, this Thesis focuses on integrating mobile robotics in the cloud, especially in the context of Industry 4.0. It also covers the investigation of the capabilities of 5G networks, the evaluation of the feasibility of simulators such as Robot Operating System (ROS) and Gazebo, and the fusion of sensor data and the design of path planning algorithms based on RL. In other words, this Thesis not only identifies and addresses the key challenges of Industry 4.0 but also presents innovative solutions and concrete hypotheses for research. Furthermore, it promotes the combination of AI and 5G to deploy real-time services, such as cooperative navigation. Thus, it addresses critical challenges and demonstrates that technological collaboration redefines efficiency and adaptability in modern industry. / This research was funded by the Research and Development Grants Program (PAID-01-19) of the Universitat Politècnica de València. The research stay of the author at Technischen Universit¨at Darmstadt (Germany) was funded by the Program of Grants for Student Mobility of doctoral students at the Universitat Politècnica de València in 2022 from Spain and by Erasmus+ Student Mobility for Traineeship 2022 / Palacios Morocho, ME. (2024). Unleashing Technological Collaboration: AI, 5G, and Mobile Robotics for Industry 4.0 Advancements [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/204748

Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically, the differential equation method for solving the generalized eigenvalue problem is used to derive a number of novel machine learning and feature extraction algorithms. The incremental eigen-solution method is used to derive a novel incremental extension of linear discriminant analysis (LDA). Further the proposed incremental version is combined with extreme learning machine (ELM) in which the ELM is used as a preprocessor before learning. In this first key contribution, the dynamic random expansion characteristic of ELM is combined with the proposed incremental LDA technique, and shown to offer a significant improvement in maximizing the discrimination between points in two different classes, while minimizing the distance within each class, in comparison with other standard state-of-the-art incremental and batch techniques. In the second contribution, the differential equation method for solving the generalized eigenvalue problem is used to derive a novel state-of-the-art purely incremental version of slow feature analysis (SLA) algorithm, termed the generalized eigenvalue based slow feature analysis (GENEIGSFA) technique. Further the time series expansion of echo state network (ESN) and radial basis functions (EBF) are used as a pre-processor before learning. In addition, the higher order derivatives are used as a smoothing constraint in the output signal. Finally, an online extension of the generalized eigenvalue problem, derived from James Stone’s criterion, is tested, evaluated and compared with the standard batch version of the slow feature analysis technique, to demonstrate its comparative effectiveness. In the third contribution, light-weight extensions of the statistical technique known as canonical correlation analysis (CCA) for both twinned and multiple data streams, are derived by using the same existing method of solving the generalized eigenvalue problem. Further the proposed method is enhanced by maximizing the covariance between data streams while simultaneously maximizing the rate of change of variances within each data stream. A recurrent set of connections used by ESN are used as a pre-processor between the inputs and the canonical projections in order to capture shared temporal information in two or more data streams. A solution to the problem of identifying a low dimensional manifold on a high dimensional dataspace is then presented in an incremental and adaptive manner. Finally, an online locally optimized extension of Laplacian Eigenmaps is derived termed the generalized incremental laplacian eigenmaps technique (GENILE). Apart from exploiting the benefit of the incremental nature of the proposed manifold based dimensionality reduction technique, most of the time the projections produced by this method are shown to produce a better classification accuracy in comparison with standard batch versions of these techniques - on both artificial and real datasets.

Cette thèse essaie d’établir un lien entre les supports utilisés dans l’enseignement/apprentissage de l’anglais de spécialité et les chances d’une appropriation effective par les apprenants. Basée sur le concept d’apprenabilité/enseignabilité de Pienemann et celui de zone proximale de développement(ZPD) de Vygotsky entre autres, cette recherche-action a permis de découvrir que les documents authentiques sont généralement d’un accès très difficile pour les apprenants, et que les documents fabriqués sont généralement plus accessibles mais requièrent des tâches à consignes claires et sans ambiguïté pour constituer de l’input compréhensible et permettre qu’il y ait réellement apprentissage. Toutefois, les apprenants censés être des experts dans le domaine de spécialité ne le sont souvent pas, et dans un tel environnement, où parfois ni l’enseignant, ni les apprenants, ne sont experts dans le domaine de spécialité, et où les résultats attendus sont généralement au-delà de la ZPD des apprenants, même avec des documents fabriqués accompagnés de tâches appropriées, l’enseignement de l’anglais de spécialité ne peut se faire avec beaucoup de succès. Dans ces conditions, faire travailler les apprenants, en autonomie ne suffit pas pour garantir la réussite de l’apprentissage. D’où la nécessité, non seulement de revoir le contenu des enseignements et de les faire cadrer véritablement avec les connaissances des apprenants dans la (les) matières(s) de spécialité, mais aussi de permettre aux enseignants de s’approprier le concept de la nécessaire autonomisation des apprenants, pour en faire un concept partagé de tous, et pour que les apprenants se mettent effectivement à apprendre. / The aim of this thesis is to show the relationship between the documents that are used in the teaching/learning of English for specific purposes and the possibility of learners to actually achieve learning. Based on the concepts of learnability/teachability of Pienemann and of Zone of Proximal Development of Vygotsky among others, this action research has led to the finding that learners find it much more difficult to understand authentic texts than pedagogic ones but the latter require tasks with very clear instructions to become understandable input. However, in an environment where learners who are supposed to be experts in their specific subjects are not, and where sometimes neither the teachers nor the learners are experts in the specific subjects, and expected results are often beyond the ZPD of students, even with non authentic texts and relevant tasks with clear instructions, one cannot be certain to teach successfully. In such contexts, independent learning is not enough to guarantee success. It is therefore necessary to revise and adapt learning contents to the knowledge of learners in their specific subjects, and also to make sure the conceptof independent learning is adopted by all teachers so as to actually enable students to begin learning.

Distance education (DE) is now recognised as an education system independent from conventional face-to-face education. It has self-governing theories and pedagogies. It attracts students with unique characteristics different from those of on-campus students. The present distance learning student differs from the past ones by characteristics, needs and contexts. Not all students registering for distance learning conform to the characteristics of distance students described in theory. It is now acknowledged that DE systems demand special skills including time management, self-regulation and independent learning skills. Yet, few of these students enter into distance learning with prior experiences on its demands. The new student is compelled to learn to deal with challenges that come with DE i.e. the impact of ‘distance’, isolation and time management. ‘The net generation’ and ‘digital natives’ are now in college but with skills not automatically transferable to learning technologies. Information and communication technologies (ICT) providers are mostly focused on the ‘use’ rather than the ‘user’. Universities are continuously adopting new technologies leaving the student bewildered as to the focus; learning or technology training. The internet has ‘everything’; open course ware (OCW), open education resources (OERS), wikis and all web information. Students cannot simply find things for themselves. Furthermore distance learning has no policy on how to engage with the internet and students are left to decide what, which and how much is required for any level of study. Most universities in Africa moving from single to dual mode have not integrated distance learning pedagogy which requires restructuring in the organisation, policy and course development. DE, though spanning over two centuries has been mutually dependent on technology. The present technology demands a paradigm shift from that of correspondence days. These issues have created the need for support strategies that can literally accompany the DE student throughout his/her academic journey. Universities have established DE units, campuses and schools for a variety of reasons. It is required that such universities provide learner support systems for their students. The purpose of this study was to assess the learner support services available for distance learning undergraduate students in two universities in Kenya i.e. Northern University (NU) and Western University (WU). A Learner support system can comprise of numerous components. In this study, nine (9) components/indices were tested as the indices for providing support services. These are:- registration procedures, orientation programme and skills training, technology and learning materials, counselling and mentorship, interactions and communication, feedback, regional centres and library, students association and representation and course progression and satisfaction. This study employed an evaluation research design utilising both quantitative and qualitative methods. Online questionnaires were used for quantitative data collection. For qualitative data collection two (2) instruments were used; an interview schedule for key programme implementers and a documentary analysis tool for documents and websites. The findings indicated that the main indices that distinguished the two universities were registration process, technology and learning materials, counselling/mentorship and regional centres where the t-test showed significant differences. The p values were 0.008, 0.012, 0.036 and 0.015 respectively at 0.05 significance level. In all of them, Northern University (NU) had a relatively high mean score than Western University (WU) except for the index on counselling and mentorship. / Educational Foundations / D. Ed. (Socio-Education)

Distance education (DE) is now recognised as an education system independent from conventional face-to-face education. It has self-governing theories and pedagogies. It attracts students with unique characteristics different from those of on-campus students. The present distance learning student differs from the past ones by characteristics, needs and contexts. Not all students registering for distance learning conform to the characteristics of distance students described in theory. It is now acknowledged that DE systems demand special skills including time management, self-regulation and independent learning skills. Yet, few of these students enter into distance learning with prior experiences on its demands. The new student is compelled to learn to deal with challenges that come with DE i.e. the impact of ‘distance’, isolation and time management. ‘The net generation’ and ‘digital natives’ are now in college but with skills not automatically transferable to learning technologies. Information and communication technologies (ICT) providers are mostly focused on the ‘use’ rather than the ‘user’. Universities are continuously adopting new technologies leaving the student bewildered as to the focus; learning or technology training. The internet has ‘everything’; open course ware (OCW), open education resources (OERS), wikis and all web information. Students cannot simply find things for themselves. Furthermore distance learning has no policy on how to engage with the internet and students are left to decide what, which and how much is required for any level of study. Most universities in Africa moving from single to dual mode have not integrated distance learning pedagogy which requires restructuring in the organisation, policy and course development. DE, though spanning over two centuries has been mutually dependent on technology. The present technology demands a paradigm shift from that of correspondence days. These issues have created the need for support strategies that can literally accompany the DE student throughout his/her academic journey. Universities have established DE units, campuses and schools for a variety of reasons. It is required that such universities provide learner support systems for their students. The purpose of this study was to assess the learner support services available for distance learning undergraduate students in two universities in Kenya i.e. Northern University (NU) and Western University (WU). A Learner support system can comprise of numerous components. In this study, nine (9) components/indices were tested as the indices for providing support services. These are:- registration procedures, orientation programme and skills training, technology and learning materials, counselling and mentorship, interactions and communication, feedback, regional centres and library, students association and representation and course progression and satisfaction. This study employed an evaluation research design utilising both quantitative and qualitative methods. Online questionnaires were used for quantitative data collection. For qualitative data collection two (2) instruments were used; an interview schedule for key programme implementers and a documentary analysis tool for documents and websites. The findings indicated that the main indices that distinguished the two universities were registration process, technology and learning materials, counselling/mentorship and regional centres where the t-test showed significant differences. The p values were 0.008, 0.012, 0.036 and 0.015 respectively at 0.05 significance level. In all of them, Northern University (NU) had a relatively high mean score than Western University (WU) except for the index on counselling and mentorship. / Educational Foundations / D. Ed. (Socio-Education)