La organización de la tarea didáctica : La planificación

La formación en didáctica y práctica de la Geografía de los profesores en la Universidad Nacional del Sur es concebida como un proceso complejo e inacabado de construcción personal del conocimiento, en el que el ejercicio del análisis reflexivo representa la acción vertebradora. La asignatura Didáctica y Práctica de la Geografía se presenta como un saber en construcción que permite conectar la Geografía como ciencia académica con la Geografía como disciplina en la educación secundaria obligatoria, a través del enlace de los diferentes componentes que se conjugan en el proceso de enseñanza-aprendizaje con el sustento epistemológico, nexo fundamental del proceso. Una de las finalidades formativa de la disciplina es promover mediante acciones concretas un proceso que favorezca un posicionamiento del futuro docente como "autor y protagonista" en la enseñanza de la Geografía, y no como mero aplicador o receptor de diseños y líneas de acciones pensadas por otros actores, muchos de los cuales son ajenos al ámbito del trabajo en el aula. En relación con esta aspiración, a través de diferentes propuestas didácticas se aborda, desde el tratamiento teórico y desde el ejercicio de la práctica, la importancia de diagramar contenidos geográficos para la educación secundaria. De este modo, se intenta construir y fortalecer en el alumno un pensamiento consciente y fundamentado a partir del cual sea posible tomar decisiones para organizar la tarea de enseñanza y aprendizaje

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

UPM system for WMT 2012

This paper describes the UPM system for the Spanish-English translation task at the NAACL 2012 workshop on statistical machine translation. This system is based on Moses. We have used all available free corpora, cleaning and deleting some repetitions. In this paper, we also propose a technique for selecting the sentences for tuning the system. This technique is based on the similarity with the sentences to translate. With our approach, we improve the BLEU score from 28.37% to 28.57%. And as a result of the WMT12 challenge we have obtained a 31.80% BLEU with the 2012 test set. Finally, we explain different experiments that we have carried out after the competition.

Survey on integration of Applied Geology and Geomorphology in Civil Engineering curricula in the framework of the European Higher Education Area (EHEA) by using practical trips

The engineer must have sufficient theoretical knowledge to be applied to solve specific problems, with the necessary capacity to simplify these approaches, and taking into account factors such as speed, simplicity, quality and economy. In Geology, its ultimate goal is the exploration of the history of the geological events through observation, deduction, reasoning and, in exceptional cases by the direct underground exploration or experimentation. Experimentation is very limited in Geology. Reproduction laboratory of certain phenomena or geological processes is difficult because both time and space become a large scale. For this reason, some Earth Sciences are in a nearly descriptive stage whereas others closest to the experimental, Geophysics and Geochemistry, have assimilated progress experienced by the physics and chemistry. Thus, Anglo-Saxon countries clearly separate Engineering Geology from Geological Engineering, i.e. Applied Geology to the Geological Engineering concepts. Although there is a big professional overlap, the first one corresponds to scientific approach, while the last one corresponds to a technological one. Applied Geology to Engineering could be defined as the Science and Applied Geology to the design, construction and performance of engineering infrastructures in and field geology discipline. There has been much discussion on the primacy of theory over practice. Today prevails the exaggeration of practice, but you get good workers and routine and mediocre teachers. This idea forgets too that teaching problem is a problem of right balance. The approach of the action lines on the European Higher Education Area (EHEA) framework provides for such balance. Applied Geology subject represents the first real contact with the physical environment with the practice profession and works. Besides, the situation of the topic in the first trace of Study Plans for many students implies the link to other subjects and topics of the career (tunnels, dams, groundwater, roads, etc). This work analyses in depth the justification of such practical trips. It shows the criteria and methods of planning and the result which manifests itself in pupils. Once practical trips experience developed, the objective work tries to know about results and changes on student’s motivation in learning perspective. This is done regardless of the outcome of their knowledge achievements assessed properly and they are not subject to such work. For this objective, it has been designed a survey about their motivation before and after trip. Survey was made by the Unidad Docente de Geología Aplicada of the Departamento de Ingeniería y Morfología del Terreno (Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid). It was completely anonymous. Its objective was to collect the opinion of the student as a key agent of learning and teaching of the subject. All the work takes place under new teaching/learning criteria approach at the European framework in Higher Education. The results are exceptionally good with 90% of student’s participation and with very high scores in a number of questions as the itineraries, teachers and visited places (range of 4.5 to 4.2 in a 5 points scale). The majority of students are very satisfied (average of 4.5 in a 5 points scale).

Fusión de redes Bayesianas Gaussianas

Las redes Bayesianas constituyen un modelo ampliamente utilizado para la representación de relaciones de dependencia condicional en datos multivariantes. Su aprendizaje a partir de un conjunto de datos o expertos ha sido estudiado profundamente desde su concepción. Sin embargo, en determinados escenarios se demanda la obtención de un modelo común asociado a particiones de datos o conjuntos de expertos. En este caso, se trata el problema de fusión o agregación de modelos. Los trabajos y resultados en agregación de redes Bayesianas son de naturaleza variada, aunque escasos en comparación con aquellos de aprendizaje. En este documento, se proponen dos métodos para la agregación de redes Gaussianas, definidas como aquellas redes Bayesianas que modelan una distribución Gaussiana multivariante. Los métodos presentados son efectivos, precisos y producen redes con menor cantidad de parámetros en comparación con los modelos obtenidos individualmente. Además, constituyen un enfoque novedoso al incorporar nociones exploradas tradicionalmente por separado en el estado del arte. Futuras aplicaciones en entornos escalables hacen dichos métodos especialmente atractivos, dada su simplicidad y la ganancia en compacidad de la representación obtenida.---ABSTRACT---Bayesian networks are a widely used model for the representation of conditional dependence relationships among variables in multivariate data. The task of learning them from a data set or experts has been deeply studied since their conception. However, situations emerge where there is a need of obtaining a consensuated model from several data partitions or a set of experts. This situation is referred to as model fusion or aggregation. Results about Bayesian network aggregation, although rich in variety, have been scarce when compared to the learning task. In this context, two methods are proposed for the aggregation of Gaussian Bayesian networks, that is, Bayesian networks whose underlying modelled distribution is a multivariate Gaussian. Both methods are effective, precise and produce networks with fewer parameters in comparison with the models obtained by individual learning. They constitute a novel approach given that they incorporate notions traditionally explored separately in the state of the art. Future applications in scalable computer environments make such models specially attractive, given their simplicity and the gaining in sparsity of the produced model.

Memory-enhancing effects of secreted forms of the β-amyloid precursor protein in normal and amnestic mice

When administered intracerebroventricularly to mice performing various learning tasks involving either short-term or long-term memory, secreted forms of the β-amyloid precursor protein (APPs751 and APPs695) have potent memory-enhancing effects and block learning deficits induced by scopolamine. The memory-enhancing effects of APPs were observed over a wide range of extremely low doses (0.05-5,000 pg intracerebroventricularly), blocked by anti-APPs antisera, and observed when APPs was administered either after the first training session in a visual discrimination or a lever-press learning task or before the acquisition trial in an object recognition task. APPs had no effect on motor performance or exploratory activity. APPs695 and APPs751 were equally effective in the object recognition task, suggesting that the memory-enhancing effect of APPs does not require the Kunitz protease inhibitor domain. These data suggest an important role for APPss on memory processes.

Deficits in memory and motor performance in synaptotagmin IV mutant mice

Synaptotagmin (Syt) IV is a synaptic vesicle protein. Syt IV expression is induced in the rat hippocampus after systemic kainic acid treatment. To examine the functional role of this protein in vivo, we derived Syt IV null [Syt IV(−/−)] mutant mice. Studies with the rotorod revealed that the Syt IV mutants have impaired motor coordination, a result consistent with constitutive Syt IV expression in the cerebellum. Because Syt IV is thought to modulate synaptic function, we also have examined Syt IV mutant mice in learning and memory tests. Our studies show that the Syt IV mutation disrupts contextual fear conditioning, a learning task sensitive to hippocampal and amygdala lesions. In contrast, cued fear conditioning is normal in the Syt IV mutants, suggesting that this mutation did not disrupt amygdala function. Conditioned taste aversion, which also depends on the amygdala, is normal in the Syt IV mutants. Consistent with the idea that the Syt IV mutation preferentially affects hippocampal function, Syt IV mutant mice also display impaired social transmission of food preference. These studies demonstrate that Syt IV is critical for brain function and suggest that the Syt IV mutation affects hippocampal-dependent learning and memory, as well as motor coordination.

Gene-experience interaction alters the cholinergic septohippocampal pathway of mice

Spatial learning requires the septohippocampal pathway. The interaction of learning experience with gene products to modulate the function of a pathway may underlie use-dependent plasticity. The regulated release of nerve growth factor (NGF) from hippocampal cultures and hippocampus, as well as its actions on cholinergic septal neurons, suggest it as a candidate protein to interact with a learning experience. A method was used to evaluate NGF gene-experience interaction on the septohippocampal neural circuitry in mice. The method permits brain region-specific expression of a new gene by using a two-component approach: a virus vector directing expression of cre recombinase; and transgenic mice carrying genomic recombination substrates rendered transcriptionally inactive by a “floxed” stop cassette. Cre recombinase vector delivery into transgenic mouse hippocampus resulted in recombination in 30% of infected cells and the expression of a new gene in those cells. To examine the interaction of the NGF gene and experience, adult mice carrying a NGF transgene with a floxed stop cassette (NGFXAT) received a cre recombinase vector to produce localized unilateral hippocampal NGF gene expression, so-called “activated” mice. Activated and control nonactivated NGFXAT mice were subjected to different experiences: repeated spatial learning, repeated rote performance, or standard vivarium housing. Latency, the time to complete the learning task, declined in the repeated spatial learning groups. The measurement of interaction between NGF gene expression and experience on the septohippocampal circuitry was assessed by counting retrogradely labeled basal forebrain cholinergic neurons projecting to the hippocampal site of NGF gene activation. Comparison of all NGF activated groups revealed a graded effect of experience on the septohippocampal pathway, with the largest change occurring in activated mice provided with repeated learning experience. These data demonstrate that plasticity of the adult spatial learning circuitry can be robustly modulated by experience-dependent interactions with a specific hippocampal gene product.

Spatial memory is related to hippocampal subcellular concentrations of calcium-dependent protein kinase C isoforms in young and aged rats

Relationships were examined between spatial learning and hippocampal concentrations of the α, β2, and γ isoforms of protein kinase C (PKC), an enzyme implicated in neuronal plasticity and memory formation. Concentrations of PKC were determined for individual 6-month-old (n = 13) and 24-month-old (n = 27) male Long–Evans rats trained in the water maze on a standard place-learning task and a transfer task designed for rapid acquisition. The results showed significant relationships between spatial learning and the amount of PKC among individual subjects, and those relationships differed according to age, isoform, and subcellular fraction. Among 6-month-old rats, those with the best spatial memory were those with the highest concentrations of PKCγ in the particulate fraction and of PKCβ2 in the soluble fraction. Aged rats had increased hippocampal PKCγ concentrations in both subcellular fractions in comparison with young rats, and memory impairment was correlated with higher PKCγ concentrations in the soluble fraction. No age difference or correlations with behavior were found for concentrations of PKCγ in a comparison structure, the neostriatum, or for PKCα in the hippocampus. Relationships between spatial learning and hippocampal concentrations of calcium-dependent PKC are isoform-specific. Moreover, age-related spatial memory impairment is associated with altered subcellular concentrations of PKCγ and may be indicative of deficient signal transduction and neuronal plasticity in the hippocampal formation.

EFEITOS DOS ÓLEOS ESSENCIAIS DE ALECRIM (Rosmarinus officinalis) E PETITGRAIN (Citrus aurantium L.) EM MODELOS COMPORTAMENTAIS DE ATIVIDADE MOTORA, DEPRESSÃO, ANSIEDADE E APRENDIZADO EM RATOS

Apesar de várias evidências do potencial terapêutico dos óleos essenciais em diversas patologias, inclusive em transtornos mentais, os estudos científicos que comprovam esse potencial ainda são escassos. O objetivo deste trabalho foi investigar e comparar de forma sistemática os efeitos dos óleos essenciais de alecrim (Rosmarinus officinalis) e petitgrain (Citrus aurantium L.) em modelos animais com ratos nos seguintes parâmetros: atividade motora, depressão, ansiedade e aprendizado. Método: foram utilizados 297 ratos em todo o estudo, sendo: 54 no piloto 1; 66 no piloto 2; 36 no campo aberto; 36 na esquiva discriminativa; 36 no teste de enterrar esferas; 33 na natação forçada e 36 no experimento de aprendizagem. Os principais resultados revelaram que: ratos tratados com 100mg/kg (i.p.) de óleo essencial de alecrim não apresentaram diferença na atividade motora avaliada em campo aberto (p=0.213 teste de Mann-Whitney), tampouco na aprendizagem da resposta de pressão à barra em caixa de Skinner (p=0.098 teste de Mann-Whitney), comparados aos ratos controles que receberam salina 0,9% (1 mL/kg), porém esse mesmo tratamento foi efetivo em modelos de depressão (p=0.006 teste de Mann-Whitney) e ansiedade (teste de esconder esferas - p=0.003 ANOVA). No que diz respeito ao óleo essencial de petitgrain administrado em ratos na dose de 30mg/kg (i.p.), não observou-se diferença na atividade motora (p=0.795 teste de Mann-Whitney), contudo obteve-se efeito ansiolítico (teste de esconder esferas - p=0.028 ANOVA) e antidepressivo (p=0.001 teste de Mann-Whitney) em relação ao controle. Ademais, o óleo de petitgrain proporcionou uma melhora na aprendizagem (p=0.002 teste de Mann-Whitney) se comparado com os animais do grupo controle e os animais tratados com alecrim. Dessa forma podemos concluir que ambos os óleos estudados (alecrim e petitgrain) apresentaram atividades ansiolítica e antidepressiva nos testes realizados e apenas o óleo de petitgrain produziu efeitos na aprendizagem dos animais.

Embedded use of iPads to enhance student engagement and retention in technical education

This paper focusses on attracting and retaining young people into technical disciplines. It introduces a new model of technical education from age 14 that the UK Government initiated in 2008. A concept of University led Technical Colleges (UTCs) for 14-19 year olds. These state supported schools, sponsored by a University, have technical curricula, technologically enabled learning environments and strong engagement with employers. As new schools they have been able to recruit outstanding staff that are conversant with the use of technology to enhance learning and all students have their own iPads. The Aston University Engineering Academy opened in September 2012 and a recent survey of staff, students and parents has provided both qualitative and quantitative data on the benefits to motivation and learning of these embedded iPads. The devices have also had advantages for the management of data on student achievement from a leadership, teaching staff and parental view point.

Classification-Based Method of Linear Multicriteria Optimization

The paper describes a classification-based learning-oriented interactive method for solving linear multicriteria optimization problems. The method allows the decision makers describe their preferences with greater flexibility, accuracy and reliability. The method is realized in an experimental software system supporting the solution of multicriteria optimization problems.

Normalização de dispositivos móveis no processo de aprendizagem de inglês : um estudo à luz da complexidade

We live in a world inherently influenced by technology and in which education is immersed in realities made possible by the support of digital technologies, such as electronic mobile devices. Thus, the general aim of this study lies in mapping and analysing the influence of mobile devices on teaching, especially with reference to learning the English language. The specific aims are to investigate how the use of mobile devices is present in the research participants’ practices, consider whether such use is beneficial, according to the students, to the English language learning as well as mapping how the use of mobile devices favours the normalisation stage, taken in this research as a complex process.The theoretical background of this study includes the premises of the Paradigm of Complexity, especially concerning the acquisition of a second language, as well as the precepts of Normalisation, which is related to the total integration of digital technologies into the English teaching and learning process in such a way that they become invisible, and the theories of language learning mediated by computers and mobile devices. Methodologically, this is an ethnographic qualitative research and its context is a language institute located in the Triângulo Mineiro region. In addition to students from five groups in the institution, two teachers and an administrative assistant participated in the survey. Data was collected through an online questionnaire, learning reports produced by students and interviews with teachers and administrative staff. The analyses indicate that mobile devices are present in the daily practices of English learners, but these uses, in most cases, are carried out through the teacher's encouragement. Moreover, despite having positive sayings on the role of digital technologies in the process of English teaching and learning, there is, among students and teachers, a dichotomy between saying and doing about the learning contexts considered valid. Additionally, the use of mobile devices in the English learning process is not yet established as a normalised issue because the process of integration of technology in teaching is still ruled by traditional uses of the technology. I conclude that the use of mobile devices in the English learning process is still not normalised, because even if students use their mobile devices every day, they generally do not realize the affordances of such use as possibilities to learn English.

Instructional Gaming

This action research will investigate instructional games as a strategy to increase third grade students’ engagement and motivation. A researcher-created behavior checklist and survey will document students’ behavior and attitudes during baseline, intervention, and post intervention. Analysis will investigate changes in engagement, motivation, and grades due to the gaming intervention.

Wie lässt sich das Interesse am Erlernen von Deutsch als Fremdsprache (wieder) steigern? : Warum lernt man als schwedischer Schüler neben der eigenen Muttersprache gerade Deutsch als zweite Fremdsprache? Was sind Anreize dafür?

By the means of a questionnaire the present work examines the attitudes among pupils between the 5th and 9th grade towards choosing French, Spanish or German as their third language. The main question to be answered is "What needs to be improved to raise the interest in choosing specifically German as their preferred third language?". The other questions posed are for example "Do they want to study a language at all?", "Which language do they want to study and why?" or "What motivates them to keep studying generally?". The results show a high motivation and that the most pupils have already decided for a specific language at the middle of the 5th grade. Family and friends play a crucial role in choosing their language in combination with other factors such as the experiences of visiting countries or settings where the target language is used. To raise the popularity of German as the chosen language is not a short time project. More variation in teaching and real contact with German people, for instance language trips, needs to be done or improved. Nearly all of the pupils want to use modern techniques like chat or video conversations instead of just reading a text book.