Responding to Students’ Errors in Online Practical Translation Classes

Author:

Rasul Sabir

Abstract

In practical translation classes it is inevitable that students make translation errors. Making errors is in fact a characteristic of translation training process, and the role of the teacher is to respond and offer timely and appropriate correction/feedback so that students are able to differentiate between correct and erroneous translations. Training students to develop the ability to produce correct and accurate translation is part and parcel of any practical translation classes. This paper, which has a pedagogical nature, investigates the treatment of students’ errors in online practical translation classes. It extends the area of ‘responding to errors’ to translation studies, on the one hand, and to online classes, on the other hand. Following Thompson’s (2007) model of responding to errors, the paper attempts to find out when and how teachers respond to translation errors made by students in online English-Kurdish practical translation classes. The results show that the teachers respond to the vast majority of translation errors occurred in the course of the online classes. In terms of time, the teachers never interrupted students instantly but waited until the end of translation units or utterances and then responded to the errors. In terms of the method of responding, the results revealed that the teachers mostly focused on meaning and used various techniques of error responding, including students’ involvement and offering their own corrections (with or without feedback). These results, coupled with critical comments provided, are hoped to offer useful insights to would-be translation teachers and trainers to better understand how and when to respond to students’ errors in online practical translation classes.

Publisher

University of Human Development

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Localization Recommendation Algorithm of Online Translation Course Based on Deep Neural Network;2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC);2023-06-16

2. Design and Implementation of English Online Translation Cloud Platform;2022 International Conference on Education, Network and Information Technology (ICENIT);2022-09

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