Due to the Covid-19 pandemic and considerable reliance on virtual education and communication, the language acquisition contexts are focusing on the transformational shift in pedagogies applying multilingual communicative capacities like ‘translanguaging’. P More
Due to the Covid-19 pandemic and considerable reliance on virtual education and communication, the language acquisition contexts are focusing on the transformational shift in pedagogies applying multilingual communicative capacities like ‘translanguaging’. Perhaps the best academic resources to explore about the issue are translation-oriented courses taught through typical teaching strategies in academic English programs. The issue led to reinforcement of considering pedagogical translanguaging within English Language Teaching (ELT) context among EFL learners who attend the related courses of the ELT programs focusing on translation skills. Thus, a sequential explanatory mixed design was selected to study the possible transformation resulted by translanguaging among EFL learners and teachers in the current study. As the pedagogical implications of the study, it is possible to declare that translanguaging within EFL context is considered as an influential strategy in helping teachers and learners to benefit from bilingual capacities in providing and understanding the content of courses. The issue also emphasizes on the transformational shift among the scholars and experts of the EFL context to have a new look over the role of translation, as an interactive code-switching procedure between First Language (L1) and Second Language (L2), which is not banning the language learners’ linguistic repertoire application.
Manuscript profile
The current study investigated Persian-English translations conducted by a human translator and a machine translator. The researchers employed House’s Translation Quality Assessment (TQA) model to evaluate the differences between the two translated works. Accordin More
The current study investigated Persian-English translations conducted by a human translator and a machine translator. The researchers employed House’s Translation Quality Assessment (TQA) model to evaluate the differences between the two translated works. Accordingly, they had the Persian texts translated by a human translator and Google Machine Translator (GMT). The translation quality, error recognition, and mismatches of the two translations were subsequently analyzed. The results showed a one-to-one match between the source and target texts regarding the human translator’s work. Furthermore, the results revealed both overt and covert errors when comparing the human and machine translators. The error analysis results also suggested that the quality of the output provided by the GMT can cause misunderstanding in the meaning. Academic texts could mean different in various contexts. Hence, it is necessary to consider human interferences when dealing with the genre of the academic text.
Manuscript profile
