Aprender nuevas palabras en un idioma extranjero, es decir, el léxico necesario que fundamenta la posibilidad del desarrollo de las destrezas comunicativas, constituye uno de los problemas más complejos en el proceso tanto de enseñanza como de aprendizaje del español como lengua extranjera. En relación con el aprendizaje del vocabulario identificamos un posible problema; el riesgo de que el número de palabras aprendidas se olvide aumenta después de la prueba o los ejercicios. Si nuestros alumnos no pueden ampliar su vocabulario su competencia comunicativa tampoco va a desarrollar.Para poder entender por qué ocurre el problema y cómo se podría encontrar otros recursos didácticos que contribuyan a un cambio en el proceso, investigamos un fenómeno conocido por la psicología de la educación como el efecto de la memoria espaciada - un fenómeno cognitivo que se benéfica de las repeticiones, pero siempre distribuidas en el tiempo. Estrategias de enseñanza que utilizan dicho efecto se refiere como aprendizaje distribuido.Mediante un pequeño estudio analizamos el efecto de la memoria espaciada (ME) como método alternativa. De este estudio podemos inferir que existe un efecto de memoria espaciada tangible en el aprendizaje de los alumnos que estudiaron según un modelo distribuido, es decir con repeticiones.Pudimos constatar un resultado positivo en este pequeño estudio piloto. Los alumnos lograron recordar en la examinación el 85% de las palabras ejercitadas en la clase un mes después. Este resultado abre nuevas perspectivas de estudio e indica que puede haber alternativas didácticas en la enseñanza del vocabulario de ELE en el salón escolar sueco. / Studying and learning words in a foreign language in order to develop a vocabulary that promotes communicative competence, is a daunting task both for students and teachers of Spanish as a foreign language in Sweden and elsewhere. In this context we identify one problem; the possibility that words learned in class will be forgotten as soon as they have been tested on a quiz or exam. If our students cannot incorporate new words into their vocabulary it is quite possible that their communicative development will stop or slow down.       In order to understand this problem and find alternative ways to teach vocabulary we investigated a phenomena called ‘the spaced memory effect’. In the field of educational psychology this is when the learner study with repetitions distributed over time. Practices that build on the spaced memory effect are often called distributed learning.       In a small study we tested this effect as an alternative way of teaching and learning vocabulary. From this study we could conclude that the effect is possible to measure and that it is consistent in all the test subjects that followed a study model that was based on repetitions or a distributed learning model. It was shown that after one month the students were able to remember 85% of the words.       The results from this small study provides new perspectives for further investigation and suggests that there are alternative ways of teaching Spanish vocabulary in Swedish schools.

[pt] As redes da Internet das Coisas (IdC) incluem dispositivos inteligentes que contêm muitos sensores que permitem interagir com o mundo físico, coletando e processando dados de streaming em tempo real. O consumo total de energia e o custo desses sensores afetam o consumo de energia e o custo dos dispositivos IdC. O tipo de sensor determina a precisão da interface analógica e a resolução dos conversores analógico-digital (ADCs). A resolução dos ADCs tem um compromisso entre a precisão de inferência e o consumo de energia, uma vez que o consumo de energia dos ADCs depende do número de bits usados para representar amostras digitais. Nesta tese, apresentamos um esquema de aprendizado distribuído com eficiência energética usando sinais quantizados para redes da IdC. Em particular, desenvolvemos algoritmos de gradiente estocástico com reconhecimento de quantização distribuído (DQA-LMS) e de mínimos quadrados recursivos com reconhecimento de quantização distribuído (DQA-RLS) que podem aprender parâmetros de maneira eficiente em energia usando sinais quantizados com poucos bits, exigindo um baixo custo computacional. Além disso, desenvolvemos uma estratégia de compensação de viés para melhorar ainda mais o desempenho dos algoritmos propostos. Uma análise estatística dos algoritmos propostos juntamente com uma avaliação da complexidade computacional das técnicas propostas e existentes é realizada. Os resultados numéricos avaliam os algoritmos com reconhecimento de quantização distribuída em relação às técnicas existentes para uma tarefa de estimação de parâmetros em que os dispositivos IdC operam em um modo ponto a ponto. Também apresentamos um esquema de aprendizado federativo com eficiência energética usando sinais quantizados para redes de IdC. Desenvolvemos o algoritmo federated averaging LMS (QA-FedAvg-LMS) com reconhecimento de quantização para redes IdC estruturadas por configuração de aprendizado federativo em que os dispositivos IdC trocam suas estimativas com um servidor. Uma estratégia de compensação de viés para QA-FedAvg-LMS é proposta junto com sua análise estatística e a avaliação de desempenho em relação às técnicas existentes com resultados numéricos. / [en] Internet of Things (IoT) networks include smart devices that contain many sensors that allow them to interact with the physical world, collecting and processing streaming data in real time. The total energy-consumption and cost of these sensors affect the energy-consumption and the cost of IoT devices. The type of sensor determines the accuracy of the analog interface and the resolution of the analog-to-digital converters (ADCs). The ADC resolution requirement has a trade-off between sensing performance and energy consumption since the energy consumption of ADCs strongly depends on the number of bits used to represent digital samples. In this thesis, we present an energy-efficient distributed learning framework using coarsely quantized signals for IoT networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) and a distributed quantization-aware recursive least-squares (DQA-RLS) algorithms that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Moreover, we develop a bias compensation strategy to further improve the performance of the proposed algorithms. We then carry out a statistical analysis of the proposed algorithms along with a computational complexity evaluation of the proposed and existing techniques. Numerical results assess the distributed quantization-aware algorithms against existing techniques for distributed parameter estimation where IoT devices operate in a peer-to-peer mode. We also introduce an energy-efficient federated learning framework using coarsely quantized signals for IoT networks, where IoT devices exchange their estimates with a server. We then develop the quantization-aware federated averaging LMS (QA-FedAvg-LMS) algorithm to perform parameter estimation at the clients and servers. Furthermore, we devise a bias compensation strategy for QA-FedAvg-LMS, carry out its statistical analysis, and assess its performance against existing techniques with numerical results.

The advances in ICTs have decisively changed the library and learning environment. On the one hand, ICTs have enhanced the variety and accessibility to library collections and services to break the barriers of location and time. On the other, the e-Learning has emerged as an additional medium for imparting education in many disciplines to overcome the constraint of physical capacity associated with the traditional classroom methods. For a vast developing country like India, this provides an immense opportunity to provide even higher education to remote places besides extending the library services through networking. Thanks to the recent initiatives by the public and private institutions in this direction, a few web-based instruction courses are now running in the country. This paper reviews different aspects of e-Learning and emerging learning landscapes. It further presents the library scene and new opportunities for its participation in the e-Learning process. How these ICTs driven advances can contribute to the comprehensive learning process in India is highlighted.

Libraries have traditionally facilitated each of the following elements of research: production of new knowledge, its preservation and its organization to make it accessible for use over the generations. In modern times, the library is constantly required to meet the challenges of information explosion. Assimilating resources and restructuring practices to process the large data volumes both in the print and digital form held across the globe, therefore, becomes very important. A recourse by the libraries to application of successive forms of what can be called as Digital Library Technologies (DLT) has been the imperative. The Open Archives Initiative (OAI) is one recent development that is expected to assist the libraries to partner in setting up virtual learning environment and integrating research on a near universal scale. Future extension of this concept is envisaged to be that of Grid Computing. The technologies driving the â Gridâ would let people share computing power, databases, and other on-line tools securely across institutional and geographic boundaries without sacrificing the local autonomy. Ushering an era of the ubiquitous library helping the e-research is thus on the card. This paper reviews the emerging technological changes and charts the future role for the libraries with special reference to India.