‘Learning to be Zen’: Women travellers and the imperative to happy

This paper follows the emotional management of lone, independent women travellers as they move through tourist spaces, based on my doctoral research Embodiment and Emotion in the experiences of independent women tourists (2012). Specifically, this paper will focus on ‘gendering happiness’ by arguing that women travellers are significantly compelled to feel and display characteristics of happiness, humour and ‘learning to be Zen’ in order to be successful travellers. The imperative to become, and remain, happy and humorous in the face of embodied, emotional and gendered constraints is a key feature of women’s reflections of their travelling experiences, mirroring the recent emergence of literature into happiness and positive thinking within feminist theory (Ehrenreich 2010, Ahmed 2010). Negotiating ‘bad’ emotions provides a powerful insight into the perceptions of women travellers; to remain happy can mask problematic power relations and other forms of resistance. This is not to say that emotional negotiation is not partly a form of effective resistance, rather, I wish to make room for the freedom to be unhappy and angry in travelling space without feeling failure for not achieving a successful travelling identity.

Learning Environments in Design Studio Culture

The paper reports on a study of design studio culture from a student perspective. Learning in design studio culture has been theorised variously as a signature pedagogy emulating professional practice models, as a community of practice and as a form of problem-based learning, all largely based on the study of teaching events in studio. The focus of this research has extended beyond formally recognized activities to encompass the student’s experience of their social and community networks, working places and study set-ups, to examine how these have contributed to studio culture and how there have been supported by studio teaching. Semi-structured interviews with final year undergraduate students of architecture formed the basis of the study using an interpretivist approach informed by Actor-network theory, with studio culture featured as the focal actor, enrolling students and engaging with other actors, together constituting an actor-network of studio culture. The other actors included social community patterns and activities; the numerous working spaces (including but not limited to the studio space itself); the equipment, tools of trade and material pre-requisites for working; the portfolio enrolling the other actors to produce work for it; and the various formal and informal events associated with the course itself. Studio culture is a highly charged social arena: The question is how, and in particular, which aspects of it support learning? Theoretical models of situated learning and communities of practice models have informed the analysis, with Bourdieu’s theory of practice, and his interrelated concepts of habitus, field and capital providing a means of relating individually acquired habits and modes of working to social contexts. Bourdieu’s model of habitus involves the externalisation through the social realm of habits and knowledge previously internalised. It is therefore a useful model for considering whole individual learning activities; shared repertoires and practices located in the social realm. The social milieu of the studio provides a scene for the exercise and display of ‘practicing’ and the accumulation of a form of ‘practicing-capital’. This capital is a property of the social milieu rather than the space, so working or practicing in the company of others (in space and through social media) becomes a more valued aspect of studio than space or facilities alone. This practicing-capital involves the acquisition of a habitus of studio culture, with the transformation of physical practices or habits into social dispositions, acquiring social capital (driving the social milieu) and cultural capital (practicing-knowledge) in the process. The research drew on students’ experiences, and their practicing ‘getting a feel for the game’ by exploring the limits or boundaries of the field of studio culture. The research demonstrated that a notional studio community was in effect a social context for supporting learning; a range of settings to explore and test out newly internalised knowledge, demonstrate or display ideas, modes of thinking and practicing. The study presents a nuanced interpretation of how students relate to a studio culture that involves a notional community, and a developing habitus within a field of practicing that extends beyond teaching scenarios.

Education and self-regulation of learning for gifted pupils: Systemic design and development.

Gifted pupils differ from their age-mates with respect to development potential, actual competencies, self-regulatory capabilities, and learning styles in one or more domains of competence. The question is how to design and develop education that fits and further supports such characteristics and competencies of gifted pupils. Analysis of various types of educational interventions for gifted pupils reflects positive cognitive or intellectual effects and differentiated social comparison or group-related effects on these pupils. Systemic preventive combination of such interventions could make these more effective and sustainable. The systemic design is characterised by three conditional dimensions: differentiation of learning materials and procedures, integration by and use of ICT support, and strategies to improve development and learning. The relationships to diagnostic, instructional, managerial, and systemic learning aspects are expressed in guidelines to develop or transform education. The guidelines imply the facilitation of learning arrangements that provide flexible self-regulation for gifted pupils. A three-year pilot in Dutch nursery and primary school is conducted to develop and implement the design in collaboration with teachers. The results constitute prototypes of structured competence domains and supportive software. These support the screening of entry characteristics of all four-year old pupils and assignment of adequate play and learning processes and activities throughout the school career. Gifted and other pupils are supported to work at their actual achievement or competency levels since their start in nursery school, in self-regulated learning arrangements either in or out of class. Each pupil can choose other pupils to collaborate with in small groups, at self-chosen tasks or activities, while being coached by the teacher. Formative evaluation of the school development process shows that the systemic prevention guidelines seem to improve learning and social progress of gifted pupils, including their self-regulation. Further development and implementation steps are discussed.

Is Our Students Learning - QuestionMark: Extending E-Assessment Systems for Adapative Marking and Feedback

Our key contribution is a flexible, automated marking system that adds desirable functionality to existing E-Assessment systems. In our approach, any given E-Assessment system is relegated to a data-collection mechanism, whereas marking and the generation and distribution of personalised per-student feedback is handled separately by our own system. This allows content-rich Microsoft Word feedback documents to be generated and distributed to every student simultaneously according to a per-assessment schedule.

The feedback is adaptive in that it corresponds to the answers given by the student and provides guidance on where they may have gone wrong. It is not limited to simple multiple choice which are the most prescriptive question type offered by most E-Assessment Systems and as such most straightforward to mark consistently and provide individual per-alternative feedback strings. It is also better equipped to handle the use of mathematical symbols and images within the feedback documents which is more flexible than existing E-Assessment systems, which can only handle simple text strings.

As well as MCQs the system reliably and robustly handles Multiple Response, Text Matching and Numeric style questions in a more flexible manner than Questionmark: Perception and other E-Assessment Systems. It can also reliably handle multi-part questions where the response to an earlier question influences the answer to a later one and can adjust both scoring and feedback appropriately.

New question formats can be added at any time provided a corresponding marking method conforming to certain templates can also be programmed. Indeed, any question type for which a programmatic method of marking can be devised may be supported by our system. Furthermore, since the student’s response to each is question is marked programmatically, our system can be set to allow for minor deviations from the correct answer, and if appropriate award partial marks.

Learning at the Interstices; Locating Practical Philosophies for Understanding Physical/virtual Inter-spaces

Virtual worlds are relatively recent developments, and so it is tempting to believe that they need to be understood through newly developed theories and philosophies. However, humans have long thought about the nature of reality and what it means to be “real.” This paper examines the three persistent philosophical concepts of Metaxis, Liminality and Space that have evolved across more than 2000 years of meditation, contemplation and reflection. Our particular focus here is on the nature of the interface between the virtual and the physical: at the interstices, and how the nature of transactions and transitions across those interfaces may impact upon learning. This may, at first, appear to be an esoteric pursuit, but we ground our arguments in primary and secondary data from research studies in higher education.

Qualidade de vida no trabalho da Emater-RN: validação de um instrumento síntese de pesquisa e diagnóstico

The theoretical recital of the present study it is initiated of the evidence that the work occupies an important space in the man s life in way that the majority of the people works and passes great part of its time inside organizati ons. However, it is verified that the relation between man and work is becoming increasingly disagreement a time that the employees had started to complain work s routines, stress, not use all their potential and inadequate work s conditions. It can be observed by the way of Dejours (1994) studies. Thus, as contribution for the quality of work life s (QWL) studies the research developed here objectified to characterize the public employees quality of work life at EMATER -RN taking as reference an instrumen t of research synthesized from the typical academic literature of the subject. The synthesis of an ampler instrument is a necessity not taken care to the literature that treats on the subject but already perceived by some studies like Moraes et al (1990); Rodrigues (1989); Siqueira & Coleta (1989); Moraes et al (1992); Carvalho & Souza (2003); El -Aouar & Souza (2003) and Mourão, Kilimnick & Fernandes (2005); Adorno, Marques & Borges (2005) amongst others. These studies point out weak points of the existing models in the QWL s literature, as well as they recommend the elaboration of a model more flexible, that contemplates Brazilian cultural characteristics, and that contemplates the entire variable studied in the main existing models. For reach this objectiv e the adopted methodology was characterized as a case study with collected data in qualitative and quantitative way. Questionnaires and comments had been used as sources of evidences. These evidences had been tabulated through of statistical package SPSS ( Statistical Package for Social Science), in which the main technique of multivariate analysis used were the factorial analysis. As for the gotten results, it was verified the grouping of the quality of work life s indicators in 11 factors which are: Work s execution, Individual accomplishment, Work s equity, Relation individual and organization, Work s organization, Adequacy of the remuneration, Relation between head and subordinate, Effectiveness of the communication and the learning, Relation between work and personal life, Participation and Effectiveness of the work processes. Whatever to the characterization of the EMATER -RN s quality of work life it was clearly that to the measure that the satisfaction s evaluation with the QWL in the organization walks to intrinsic factors for extrinsic factors this level of satisfaction goes diminishing what points to the importance to improve these extrinsic factors in the institution. In summary it is possible to conclude that the organization studied has offered a significant set of referring variable to the quality of work life of the individual

Bayesian nonparametric latent variable models

L’un des problèmes importants en apprentissage automatique est de déterminer la complexité du modèle à apprendre. Une trop grande complexité mène au surapprentissage, ce qui correspond à trouver des structures qui n’existent pas réellement dans les données, tandis qu’une trop faible complexité mène au sous-apprentissage, c’est-à-dire que l’expressivité du modèle est insuffisante pour capturer l’ensemble des structures présentes dans les données. Pour certains modèles probabilistes, la complexité du modèle se traduit par l’introduction d’une ou plusieurs variables cachées dont le rôle est d’expliquer le processus génératif des données. Il existe diverses approches permettant d’identifier le nombre approprié de variables cachées d’un modèle. Cette thèse s’intéresse aux méthodes Bayésiennes nonparamétriques permettant de déterminer le nombre de variables cachées à utiliser ainsi que leur dimensionnalité. La popularisation des statistiques Bayésiennes nonparamétriques au sein de la communauté de l’apprentissage automatique est assez récente. Leur principal attrait vient du fait qu’elles offrent des modèles hautement flexibles et dont la complexité s’ajuste proportionnellement à la quantité de données disponibles. Au cours des dernières années, la recherche sur les méthodes d’apprentissage Bayésiennes nonparamétriques a porté sur trois aspects principaux : la construction de nouveaux modèles, le développement d’algorithmes d’inférence et les applications. Cette thèse présente nos contributions à ces trois sujets de recherches dans le contexte d’apprentissage de modèles à variables cachées. Dans un premier temps, nous introduisons le Pitman-Yor process mixture of Gaussians, un modèle permettant l’apprentissage de mélanges infinis de Gaussiennes. Nous présentons aussi un algorithme d’inférence permettant de découvrir les composantes cachées du modèle que nous évaluons sur deux applications concrètes de robotique. Nos résultats démontrent que l’approche proposée surpasse en performance et en flexibilité les approches classiques d’apprentissage. Dans un deuxième temps, nous proposons l’extended cascading Indian buffet process, un modèle servant de distribution de probabilité a priori sur l’espace des graphes dirigés acycliques. Dans le contexte de réseaux Bayésien, ce prior permet d’identifier à la fois la présence de variables cachées et la structure du réseau parmi celles-ci. Un algorithme d’inférence Monte Carlo par chaîne de Markov est utilisé pour l’évaluation sur des problèmes d’identification de structures et d’estimation de densités. Dans un dernier temps, nous proposons le Indian chefs process, un modèle plus général que l’extended cascading Indian buffet process servant à l’apprentissage de graphes et d’ordres. L’avantage du nouveau modèle est qu’il admet les connections entres les variables observables et qu’il prend en compte l’ordre des variables. Nous présentons un algorithme d’inférence Monte Carlo par chaîne de Markov avec saut réversible permettant l’apprentissage conjoint de graphes et d’ordres. L’évaluation est faite sur des problèmes d’estimations de densité et de test d’indépendance. Ce modèle est le premier modèle Bayésien nonparamétrique permettant d’apprendre des réseaux Bayésiens disposant d’une structure complètement arbitraire.

Europeanization in VET policy as a process of reshaping the educational space

The EU represents a transforming educational space, where national and supranational boundaries in educational governance are becoming blurred. The EU has become an important actor in educational governance and an important arena for policy learning and transfer. This paper explores how the process of reshaping the educational space manifests itself in the process of the Europeanization of VET policy in the case of Estonia. In Estonia, this process was followed by the growth of executive VET institutions and has developed from rather uncritical initial policy transfer to more active learning from the EU, although conformism can still be seen in cases of the introduction of standardizing policy tools. (DIPF/Orig.)

Creating Common Ground: Architecture For Tactical Learning and Creative Convergence

Certain environments can inhibit learning and stifle enthusiasm, while others enhance learning or stimulate curiosity. Furthermore, in a world where technological change is accelerating we could ask how might architecture connect resource abundant and resource scarce innovation environments? Innovation environments developed out of necessity within urban villages and those developed with high intention and expectation within more institutionalized settings share a framework of opportunity for addressing change through learning and education. This thesis investigates formal and informal learning environments and how architecture can stimulate curiosity, enrich learning, create common ground, and expand access to education. The reason for this thesis exploration is to better understand how architects might design inclusive environments that bring people together to build sustainable infrastructure encouraging innovation and adaptation to change for years to come. The context of this thesis is largely based on Colin McFarlane’s theory that the “city is an assemblage for learning” The socio-spatial perspective in urbanism, considers how built infrastructure and society interact. Through the urban realm, inhabitants learn to negotiate people, space, politics, and resources affecting their daily lives. The city is therefore a dynamic field of emergent possibility. This thesis uses the city as a lens through which the boundaries between informal and formal logics as well as the public and private might be blurred. Through analytical processes I have examined the environmental devices and assemblage of factors that consistently provide conditions through which learning may thrive. These parameters that make a creative space significant can help suggest the design of common ground environments through which innovation is catalyzed.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

Construção da plataforma de e-Learning do Instituto Politécnico de Santarém

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.

Construção da plataforma de e-Learning do Instituto Politécnico de Santarém

Relatório de estágio apresentado para obtenção do grau de mestre em Educação e Comunicação Multimédia.

A deformation model of flexible, high area-to-mass ratio debris under perturbations and validation method

A new type of space debris was recently discovered by Schildknecht in near -geosynchronous orbit (GEO). These objects were later identified as exhibiting properties associated with High Area-to-Mass ratio (HAMR) objects. According to their brightness magnitudes (light curve), high rotation rates and composition properties (albedo, amount of specular and diffuse reflection, colour, etc), it is thought that these objects are multilayer insulation (MLI). Observations have shown that this debris type is very sensitive to environmental disturbances, particularly solar radiation pressure, due to the fact that their shapes are easily deformed leading to changes in the Area-to-Mass ratio (AMR) over time. This thesis proposes a simple effective flexible model of the thin, deformable membrane with two different methods. Firstly, this debris is modelled with Finite Element Analysis (FEA) by using Bernoulli-Euler theory called “Bernoulli model”. The Bernoulli model is constructed with beam elements consisting 2 nodes and each node has six degrees of freedom (DoF). The mass of membrane is distributed in beam elements. Secondly, the debris based on multibody dynamics theory call “Multibody model” is modelled as a series of lump masses, connected through flexible joints, representing the flexibility of the membrane itself. The mass of the membrane, albeit low, is taken into account with lump masses in the joints. The dynamic equations for the masses, including the constraints defined by the connecting rigid rod, are derived using fundamental Newtonian mechanics. The physical properties of both flexible models required by the models (membrane density, reflectivity, composition, etc.), are assumed to be those of multilayer insulation. Both flexible membrane models are then propagated together with classical orbital and attitude equations of motion near GEO region to predict the orbital evolution under the perturbations of solar radiation pressure, Earth’s gravity field, luni-solar gravitational fields and self-shadowing effect. These results are then compared to two rigid body models (cannonball and flat rigid plate). In this investigation, when comparing with a rigid model, the evolutions of orbital elements of the flexible models indicate the difference of inclination and secular eccentricity evolutions, rapid irregular attitude motion and unstable cross-section area due to a deformation over time. Then, the Monte Carlo simulations by varying initial attitude dynamics and deformed angle are investigated and compared with rigid models over 100 days. As the results of the simulations, the different initial conditions provide unique orbital motions, which is significantly different in term of orbital motions of both rigid models. Furthermore, this thesis presents a methodology to determine the material dynamic properties of thin membranes and validates the deformation of the multibody model with real MLI materials. Experiments are performed in a high vacuum chamber (10-4 mbar) replicating space environment. A thin membrane is hinged at one end but free at the other. The free motion experiment, the first experiment, is a free vibration test to determine the damping coefficient and natural frequency of the thin membrane. In this test, the membrane is allowed to fall freely in the chamber with the motion tracked and captured through high velocity video frames. A Kalman filter technique is implemented in the tracking algorithm to reduce noise and increase the tracking accuracy of the oscillating motion. The forced motion experiment, the last test, is performed to determine the deformation characteristics of the object. A high power spotlight (500-2000W) is used to illuminate the MLI and the displacements are measured by means of a high resolution laser sensor. Finite Element Analysis (FEA) and multibody dynamics of the experimental setups are used for the validation of the flexible model by comparing with the experimental results of displacements and natural frequencies.

'Bayesian Optimisation for Nurse Scheduling'

A Bayesian optimisation algorithm for a nurse scheduling problem is presented, which involves choosing a suitable scheduling rule from a set for each nurse's assignment. When a human scheduler works, he normally builds a schedule systematically following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not yet completed, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this paper, we design a more human-like scheduling algorithm, by using a Bayesian optimisation algorithm to implement explicit learning from past solutions. A nurse scheduling problem from a UK hospital is used for testing. Unlike our previous work that used Genetic Algorithms to implement implicit learning [1], the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The Bayesian optimisation algorithm is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, new rule strings have been obtained. Sets of rule strings are generated in this way, some of which will replace previous strings based on fitness. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. For clarity, consider the following toy example of scheduling five nurses with two rules (1: random allocation, 2: allocate nurse to low-cost shifts). In the beginning of the search, the probabilities of choosing rule 1 or 2 for each nurse is equal, i.e. 50%. After a few iterations, due to the selection pressure and reinforcement learning, we experience two solution pathways: Because pure low-cost or random allocation produces low quality solutions, either rule 1 is used for the first 2-3 nurses and rule 2 on remainder or vice versa. In essence, Bayesian network learns 'use rule 2 after 2-3x using rule 1' or vice versa. It should be noted that for our and most other scheduling problems, the structure of the network model is known and all variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus, learning can amount to 'counting' in the case of multinomial distributions. For our problem, we use our rules: Random, Cheapest Cost, Best Cover and Balance of Cost and Cover. In more detail, the steps of our Bayesian optimisation algorithm for nurse scheduling are: 1. Set t = 0, and generate an initial population P(0) at random; 2. Use roulette-wheel selection to choose a set of promising rule strings S(t) from P(t); 3. Compute conditional probabilities of each node according to this set of promising solutions; 4. Assign each nurse using roulette-wheel selection based on the rules' conditional probabilities. A set of new rule strings O(t) will be generated in this way; 5. Create a new population P(t+1) by replacing some rule strings from P(t) with O(t), and set t = t+1; 6. If the termination conditions are not met (we use 2000 generations), go to step 2. Computational results from 52 real data instances demonstrate the success of this approach. They also suggest that the learning mechanism in the proposed approach might be suitable for other scheduling problems. Another direction for further research is to see if there is a good constructing sequence for individual data instances, given a fixed nurse scheduling order. If so, the good patterns could be recognized and then extracted as new domain knowledge. Thus, by using this extracted knowledge, we can assign specific rules to the corresponding nurses beforehand, and only schedule the remaining nurses with all available rules, making it possible to reduce the solution space. Acknowledgements The work was funded by the UK Government's major funding agency, Engineering and Physical Sciences Research Council (EPSRC), under grand GR/R92899/01. References [1] Aickelin U, "An Indirect Genetic Algorithm for Set Covering Problems", Journal of the Operational Research Society, 53(10): 1118-1126,

“English Language Learning Strategies used by the Foreign Language Department Seminar Students Year 2006 with Either Above Eight Grade or Below it Obtained in Their Studies in the Basic Skills Area.

Nowadays language communication plays an important role in the world. For the technological explosion in the 20th century, the electronic mass media collapsed space and time barriers in human communication, enabling people to interact and live on a global scale. In this sense, the earth has been turned into a village by the electronic mass media. It not only changes the distance between countries, societies, but also shortens it between people. It means that the technological advancement makes the earth become a village. Since the distance between people is shortened, language communication becomes more important than before. To enhance language abilities, people can apply many different types of language learning strategies according to the learning styles that they have in order to learn the target language. In the Foreign Language Department of University of El Salvador Seminar students year 2006 apply different language learning strategies which make some of them get a grade either above eight or below it. To understand learning strategies, people can go back to basic term, strategy. This word comes from the ancient Greek term strategia meaning generalship or the art of war. A different, but related, word is tactics, which are tools to achieve the success of strategies. The two expressions share some basic implied characteristics: planning, competition, conscious manipulation, and movement toward a goal.