296 resultados para Turkish language--Style
Resumo:
As English increasingly becomes one of the most commonly spoken languages in the world today for a variety of economic, social and cultural reasons, education is impacted by globalisation, the internationalisation of universities and the diversity of learners in classrooms. The challenge for educators is to find more effective ways of teaching English language so that students are better able to create meaning and communicate in the target language as well as to transform knowledge and understanding into relevant skills for a rapidly changing world. This research focuses broadly on English language education underpinned by social constructivist principles informing communicative language teaching and in particular, interactive peer learning approaches. An intervention of interactive peer-based learning in two case study contexts of English as Foreign Language (EFL) undergraduates in a Turkish university and English as Second Language (ESL) undergraduates in an Australian university investigates what students gain from the intervention. Methodology utilising qualitative data gathered from student reflective logs, focus group interviews and researcher field notes emphasises student voice. The cross case comparative study indicates that interactive peer-based learning enhances a range of learning outcomes for both cohorts including engagement, communicative competence, diagnostic feedback as well as assisting development of inclusive social relationships, civic skills, confidence and self efficacy. The learning outcomes facilitate better adaptation to a new learning environment and culture. An iterative instructional matrix tool is a useful product of the research for first year university experiences, teacher training, raising awareness of diversity, building learning communities, and differentiating the curriculum. The study demonstrates that English language learners can experience positive impact through peer-based learning and thus holds an influential key for Australian universities and higher education.
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The Wikipedia has become the most popular online source of encyclopedic information. The English Wikipedia collection, as well as some other languages collections, is extensively linked. However, as a multilingual collection the Wikipedia is only very weakly linked. There are few cross-language links or cross-dialect links (see, for example, Chinese dialects). In order to link the multilingual-Wikipedia as a single collection, automated cross language link discovery systems are needed – systems that identify anchor-texts in one language and targets in another. The evaluation of Link Discovery approaches within the English version of the Wikipedia has been examined in the INEX Link the-Wiki track since 2007, whilst both CLEF and NTCIR emphasized the investigation and the evaluation of cross-language information retrieval. In this position paper we propose a new virtual evaluation track: Cross Language Link Discovery (CLLD). The track will initially examine cross language linking of Wikipedia articles. This virtual track will not be tied to any one forum; instead we hope it can be connected to each of (at least): CLEF, NTCIR, and INEX as it will cover ground currently studied by each. The aim is to establish a virtual evaluation environment supporting continuous assessment and evaluation, and a forum for the exchange of research ideas. It will be free from the difficulties of scheduling and synchronizing groups of collaborating researchers and alleviate the necessity to travel across the globe in order to share knowledge. We aim to electronically publish peer-reviewed publications arising from CLLD in a similar fashion: online, with open access, and without fixed submission deadlines.
Resumo:
Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.
Resumo:
Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.
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Beryl & Gael discuss the ‘new’ metalanguage for knowledge about language presented in the Australian Curriculum English (ACARA, 2010). Their discussion connects to practice by recounting how one teacher scaffolds her students through detailed understandings of noun and adjective groups in reading activities. The stimulus text is the novel ‘A wrinkle in time’ (L’Engle, 1962, reproduced 2007) and the purpose is to build students’ understandings so they can work towards ‘expressing and developing ideas’ in written text (ACARA, 2010).
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This paper reports results from a study exploring the multimedia search functionality of Chinese language search engines. Web searching in Chinese (Mandarin) is a growing research area and a technical challenge for popular commercial Web search engines. Few studies have been conducted on Chinese language search engines. We investigate two research questions: which Chinese language search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine's (1) features permitting Chinese language multimedia searches, (2) extent of search personalization and user control of multimedia search variables, and (3) the relationships between Web search engines and their features in the Chinese context. Key findings show that Chinese language Web search engines offer limited multimedia search functionality, and general search engines provide a wider range of features than specialized multimedia search engines. Study results have implications for Chinese Web users, Website designers and Web search engine developers. © 2009 Elsevier Ltd. All rights reserved.
Resumo:
Feedback on student performance, whether in the classroom or on written assignments, enables them to reflect on their understandings and restructure their thinking in order to develop more powerful ideas and capabilities. Research has identified a number of broad principles of good feedback practice. These include the provision of feedback that facilitates the development of reflection in learning; helps clarify what good performance is in terms of goals, criteria and expected standards; provides opportunities to close the gap between current and desired performance; delivers high quality information to students about their learning; and encourages positive motivational beliefs and self-esteem. However, high staff–student ratios and time pressures often result in a gulf between this ideal and reality. Whilst greater use of criteria referenced assessment has enabled an improvement in the extent of feedback being provided to students, this measure alone does not go far enough to satisfy the requirements of good feedback practice. Technology offers an effective and efficient means by which personalised feedback may be provided to students. This paper presents the findings of a trial of the use of the freely available Audacity program to provide individual feedback via MP3 recordings to final year Media Law students at the Queensland University of Technology on their written assignments. The trial has yielded wide acclaim by students as an effective means of explaining the exact reasons why they received the marks they were awarded, the things they did well and the areas needing improvement. It also showed that good feedback practice can be achieved without the burden of an increase in staff workload.
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The Lingodroids are a pair of mobile robots that evolve a language for places and relationships between places (based on distance and direction). Each robot in these studies has its own understanding of the layout of the world, based on its unique experiences and exploration of the environment. Despite having different internal representations of the world, the robots are able to develop a common lexicon for places, and then use simple sentences to explain and understand relationships between places even places that they could not physically experience, such as areas behind closed doors. By learning the language, the robots are able to develop representations for places that are inaccessible to them, and later, when the doors are opened, use those representations to perform goal-directed behavior.
Resumo:
In second language classrooms, listening is gaining recognition as an active element in the processes of learning and using a second language. Currently, however, much of the teaching of listening prioritises comprehension without sufficient emphasis on the skills and strategies that enhance learners’ understanding of spoken language. This paper presents an argument for rethinking the emphasis on comprehension and advocates augmenting current teaching with an explicit focus on strategies. Drawing on the literature, the paper provides three models of strategy instruction for the teaching and development of listening skills. The models include steps for implementation that accord with their respective approaches to explicit instruction. The final section of the paper synthesises key points from the models as a guide for application in the second language classroom. The premise underpinning the paper is that the teaching of strategies can provide learners with active and explicit measures for managing and expanding their listening capacities, both in the learning and ‘real world’ use of a second language.
Resumo:
This chapter reports on a study of oracy in a first-year university Business course, with particular interest in the oracy demands for second language-using international students. The research is relevant at a time when Higher Education is characterised by the confluence of increased international enrolments, more dialogic teaching and learning, and imperatives for teamwork and collaboration. Data sources for the study included videotaped lectures and tutorials, course documents, student surveys, and an interview with the lecturer. The findings pointed to a complex, oracy-laden environment where interactive talk fulfilled high-stakes functions related to social inclusion, the co-construction of knowledge, and the accomplishment of assessment tasks. The salience of talk posed significant challenges for students negotiating these core functions in their second language. The study highlights the oracy demands in university courses and foregrounds the need for university teachers, curriculum writers and speaking test developers to recognise these demands and explicate them for the benefit of all students.
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Introduction: Feeding on demand supports an infant’s innate capacity to respond to hunger and satiety cues and may promote later self-regulation of intake. Our aim was to examine whether feeding style (on demand vs to schedule) is associated with weight gain in early life. Methods: Participants were first-time mothers of healthy term infants enrolled NOURISH, an RCT evaluating an intervention to promote positive early feeding practices. Baseline assessment occurred when infants were aged 2-7 months. Infants able to be categorised clearly as feeding on demand or to schedule (mothers self report) were included in the logistic regression analysis. The model was adjusted for gender, breastfeeding and maternal age, education, BMI. Weight gain was defined as a positive difference in baseline minus birthweight z-scores (WHO standards) which indicated tracking above weight percentile. Results: Data from 356 infants with a mean age of 4.4 (SD 1.0) months were available. Of these, 197 (55%) were fed on demand, 42 (12%) were fed on schedule. There was no statistical association between feeding style and weight gain [OR=0.72 (95%CI 0.35-1.46), P=0.36]. Formula fed infants were three times more likely to be fed on schedule and formula feeding was independently associated with increased weight gain [OR=2.02 (95%CI 1.11-3.66), P=0.021]. Conclusion: In this preliminary analysis the association between feeding style and weight gain did not reach statistical significance, however , the effect size may be clinically relevant and future analysis will include the full study sample (N=698).
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With the recognition that language both reflects and constructs culture and English now widely acknowledged as an international language, the cul-tural content of language teaching materials is now being problematised. Through a quantitative analysis, this chapter focuses on opportunities for intercultural understanding and connectedness through representations of the identities that appear in two leading English language textbooks. The analyses reveal that the textbooks orientate towards British and western identities with representations of people from non-European/non-Western backgrounds being notable for their absence, while others are hidden from view. Indeed there would appear to be a neocolonialist orientation in oper-ation in the textbooks, one that aligns English with the West. The chapter proposes arguments for the consideration of cultural diversity in English language teaching (ELT) textbook design, and promoting intercultural awareness and acknowledging the contexts in which English is now being used. It also offers ways that teachers can critically reflect on existing ELT materials and proposes arguments for including different varieties of Eng-lish in order to ensure a level of intercultural understanding and connect-edness.
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Service-oriented Architectures (SOA) and Web services leverage the technical value of solutions in the areas of distributed systems and cross-enterprise integration. The emergence of Internet marketplaces for business services is driving the need to describe services, not only from a technical level, but also from a business and operational perspective. While, SOA and Web services reside in an IT layer, organizations owing Internet marketplaces are requiring advertising and trading business services which reside in a business layer. As a result, the gap between business and IT needs to be closed. This paper presents USDL (Unified Service Description Language), a specification language to describe services from a business, operational and technical perspective. USDL plays a major role in the Internet of Services to describe tradable services which are advertised in electronic marketplaces. The language has been tested using two service marketplaces as use cases.
Resumo:
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