English has for long been one of the most widely used media of communication globally, especially in the Malaysian universities. It has been termed as a Lingua Franca because it is shared with other languages which are considered first languages by different speakers. For this reason, English as a Lingua Franca (ELF) has attracted a number of researchers to investigate its variety via other languages in various communities. The objective of this paper is therefore to establish the strategies which are employing by the international students at the National University of Malaysia/ UniversitiKebangsaan Malaysia (UKM) as an example of one of the Malaysian universities; when they engage in a conversation in their classrooms to avoid the difficulty of understanding the language in the lingua Franca context. UKM has a number of Non-Native English speakers (NNESs), international students who are also multilingual and cannot understand the indigenous Malay, Chinese and Tamil languages. Therefore the mode of teaching and the way they interact with each other becomes hard for them as they try to understand each other despite their origin. This has created a need for a common and international media of communication; which can concluded by the English language. English has been identified as one of the most widely used media of communication among these students. The current article aimed to find out the strategies which employ by the multilingual students at UKM as they try to communicate to each other through conversations and discussions inside the classrooms in the context of being a lingua Franca .This objective is achieved through voice recordings of conversations made between three master students . The data is analyzed using the conversational Analysis (CA) based on Hymes’ 1972 model of speaking.
وقائع واوراق ندوة (السياسة الخارجية الامريكية في عهد اوباما) التي عقدها فرع الدراسات الدولية في 25 تشرين الثاني 2009
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This paper proposed a new method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA)) for measureing the closeness between curves. Root Mean Square Errors is used for the implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when the cov
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