Pragmatemas e a música no ensino-aprendizagem de inglês como língua adicional em um colégio estadual do RJ: papel na retenção lexical

O presente estudo analisa as contribuições do uso da música e de pragmatemas na retenção lexical por parte de três turmas de inglês como língua adicional em uma escola estadual na cidade de São João de Meriti (RJ). Para tal, constituiu três condições de pesquisa das quais fizeram parte doze alunos na turma 1 (T1); doze alunos na turma 2 (T2); e nove alunos na turma 3 (T3). A turma 1 (T1) participou de práticas pedagógicas com foco na música e sem foco explícito nos pragmatemas presentes na letra; a turma 2 (T2) de práticas pedagógicas com foco na música e nos pragmatemas da letra; e a turma 3 (T3) de práticas pedagógicas voltadas somente para o ensino-aprendizagem dos pragmatemas presentes na letra, sem o suporte da música. Anteriormente à intervenção, foi aplicado um questionário sobre os hábitos sócio-culturais dos participantes visando conhecer suas preferências musicais e perfil de letramento. Para analisar a relação entre o uso da música e de pragmatemas na retenção lexical, foram aplicados um pré-teste antes da intervenção e dois pós-testes com intervalo de 1 mês entre eles. Além disso, o estudo contou com uma entrevista semi-estruturada com os participantes, visando entender a percepção dos mesmos sobre as práticas utilizadas na intervenção. Os achados apontam que houve uma retenção ligeiramente superior nas condições T2 (música e foco nos pragmatemas) e T3 (foco exclusivo nos pragmatemas), com ligeira superioridade para a condição 3. O pragmatema recuperado com maior frequência foi "What's up", fato que pode ser parcialmente explicado pelo fato de ser pronunciado como uma palavra só e pelo número de letras que o representa ortograficamente. Estudos futuros poderão esclarecer se há significância estatística entre as diferenças encontradas assim como melhor explorar como o ensino explícito de unidades lexicais complexas pode contribuir para a retenção desses itens lexicais e consequentemente para o ensino-aprendizagem de línguas adicionais. A intervenção permitiu que os aprendizes passassem a entender que as palavras não ocorrem de modo isolado, mas que sempre caminham junto a outras. Permitiu também que a rotina pedagógica contemplasse a coconstrução do conhecimento, levando os aprendizes a reagir positivamente às práticas utilizadas no ensino de inglês, conforme relatos durante as entrevistas semi-estruturadas. Isso per se já justifica a utilização de práticas semelhantes e ilustra a contribuição do presente estudo

Language as chunks, not words

Many people think of language as words. Words are small, convenient units, especially in written English, where they are separated by spaces. Dictionaries seem to reinforce this idea, because entries are arranged as a list of alphabetically-ordered words. Traditionally, linguists and teachers focused on grammar and treated words as self-contained units of meaning, which fill the available grammatical slots in a sentence. More recently, attention has shifted from grammar to lexis, and from words to chunks. Dictionary headwords are convenient points of access for the user, but modern dictionary entries usually deal with chunks, because meanings often do not arise from individual words, but from the chunks in which the words occur. Corpus research confirms that native speakers of a language actually work with larger “chunks” of language. This paper will show that teachers and learners will benefit from treating language as chunks rather than words.

An aspect query language model based on query decomposition and high-order contextual term associations

In information retrieval (IR) research, more and more focus has been placed on optimizing a query language model by detecting and estimating the dependencies between the query and the observed terms occurring in the selected relevance feedback documents. In this paper, we propose a novel Aspect Language Modeling framework featuring term association acquisition, document segmentation, query decomposition, and an Aspect Model (AM) for parameter optimization. Through the proposed framework, we advance the theory and practice of applying high-order and context-sensitive term relationships to IR. We first decompose a query into subsets of query terms. Then we segment the relevance feedback documents into chunks using multiple sliding windows. Finally we discover the higher order term associations, that is, the terms in these chunks with high degree of association to the subsets of the query. In this process, we adopt an approach by combining the AM with the Association Rule (AR) mining. In our approach, the AM not only considers the subsets of a query as “hidden” states and estimates their prior distributions, but also evaluates the dependencies between the subsets of a query and the observed terms extracted from the chunks of feedback documents. The AR provides a reasonable initial estimation of the high-order term associations by discovering the associated rules from the document chunks. Experimental results on various TREC collections verify the effectiveness of our approach, which significantly outperforms a baseline language model and two state-of-the-art query language models namely the Relevance Model and the Information Flow model

Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows

This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

English collocations extracted from a corpus of university learners and its contribution to a language teaching pedagogy

Taking into consideration the relevance of foreign language teaching and the learning of collocations (ALTENBERG; EEG-OLOFSSON, 1990; FONTENELLE, 1994; MEUNIER; GRANGER, 2008), this paper aims at showing results of an investigation on whether the teaching of collocations should be implicit or explicit to the Brazilian university students. Furthermore, the research has the purpose of presenting some collocational aspects from a corpus of the written language learners made up of intermediate, upper intermediate and advanced university students' argumentative essays at a public university in Brazil. With the help of WordSmith Tools (SCOTT, 2007), it was possible to raise students' most frequent collocational choices and patterns, the most/least used type of collocations, the influence of the mother tongue on their choices, among other aspects. With the purpose of motivating and involving students in classroom research, it was also introduced The Corpus of Contemporary American English (COCA), created by Mark Davies. By doing so, students could compare their collocational choices with the patterns found in the online corpus, extract more collocational patterns and, consequently, be aware of the potential of corpora for the foreign learning process, specifically for raising language awareness, with focus on prefabricated chunks.

Experiences with an Object-Oriented, Multi-Stage Language

Metaphor is a multi-stage programming language extension to an imperative, object-oriented language in the style of C# or Java. This paper discusses some issues we faced when applying multi-stage language design concepts to an imperative base language and run-time environment. The issues range from dealing with pervasive references and open code to garbage collection and implementing cross-stage persistence.

The Coming of Post-reflexive Society: Commodification and Language in Digital Capitalism

Language is a unique aspect of human communication because it can be used to discuss itself in its own terms. For this reason, human societies potentially have superior capacities of co-ordination, reflexive self-correction, and innovation than other animal, physical or cybernetic systems. However, this analysis also reveals that language is interconnected with the economically and technologically mediated social sphere and hence is vulnerable to abstraction, objectification, reification, and therefore ideology – all of which are antithetical to its reflexive function, whilst paradoxically being a fundamental part of it. In particular, in capitalism, language is increasingly commodified within the social domains created and affected by ubiquitous communication technologies. The advent of the so-called ‘knowledge economy’ implicates exchangeable forms of thought (language) as the fundamental commodities of this emerging system. The historical point at which a ‘knowledge economy’ emerges, then, is the critical point at which thought itself becomes a commodified ‘thing’, and language becomes its “objective” means of exchange. However, the processes by which such commodification and objectification occurs obscures the unique social relations within which these language commodities are produced. The latest economic phase of capitalism – the knowledge economy – and the obfuscating trajectory which accompanies it, we argue, is destroying the reflexive capacity of language particularly through the process of commodification. This can be seen in that the language practices that have emerged in conjunction with digital technologies are increasingly non-reflexive and therefore less capable of self-critical, conscious change.

Pattern-based analysis of EAI languages : the case of the business modelling language

Enterprise Application Integration (EAI) is a challenging area that is attracting growing attention from the software industry and the research community. A landscape of languages and techniques for EAI has emerged and is continuously being enriched with new proposals from different software vendors and coalitions. However, little or no effort has been dedicated to systematically evaluate and compare these languages and techniques. The work reported in this paper is a first step in this direction. It presents an in-depth analysis of a language, namely the Business Modeling Language, specifically developed for EAI. The framework used for this analysis is based on a number of workflow and communication patterns. This framework provides a basis for evaluating the advantages and drawbacks of EAI languages with respect to recurrent problems and situations.