Task-based Approach in Teaching Translation: A Case Study in Jouf University

Alenezi, Abduhameed Muhatlis

doi:10.5539/hes.v10n2p189

Cited by 12 publications

(13 citation statements)

References 11 publications

(16 reference statements)

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“…A range of studies focuse on the use of task-based approach in teaching professional and language competences to students of different specialitiesmechanical engineering, aviation, translation and others (Li, 2013;Alenezi, 2020;Bystrova et al, 2019;Lytovchenko et al, 2018). Scholars have studied the use of task-based approach in the development of various language skillslistening, speaking, reading, writing, grammar and others.…”

Section: Introductionmentioning

confidence: 99%

Use of Task-Based Approach in Teaching Vocabulary to Business English Learners at University

Nychkalo

Jin-ba

Lukianova

et al. 2020

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The purpose of this study is to identify the effectiveness of the task-based approach to teaching business vocabulary to master's degree students while studying business English. The discipline of business English aims to develop students' ability to analyse and solve problems of the business environment, improve their foreign language competence on the basis of acquired business vocabulary. The authors used the task-based approach in teaching business vocabulary which was aimed at encouraging students to social contacts, discussions, use of the necessary lexical units in situations close to real business life. The participants of the study were 34 students who studied for a master's degree at the National Aviation University (Kyiv, Ukraine) majoring in aviation, rocket and space technology. The study of the course required 34 academic hours. According to the methodology of the experiment, the two groups of students (control and experimental ones) had the same learning materials during the course, but the teaching methods were different. In the control group, we used traditional vocabulary learning methods which implied the presentation of the new vocabulary by the teacher, practice exercises which made learners more familiar with the lexical units and the use of the new vocabulary by students in context. In the experimental group, task-based learning method was used which required students to work on solving real business problems with special attention to the development of teamwork skills and communication skills based on the students' experience of work in business and communication with colleagues from other countries. Specially designed tasks were used to intensify the process of learning business vocabulary. The study showed significant positive dynamics in the acquisition of business vocabulary by students of the experimental group. The results of the test confirmed that task-based approach to vocabulary learning allows students to learn the necessary business vocabulary on a particular topic more easily and quickly It has been concluded that the task-based approach is not only efficient in vocabulary learning, but also increases the students' confidence in their professional and communicative competence.

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Section: Introductionmentioning

confidence: 99%

Use of Task-Based Approach in Teaching Vocabulary to Business English Learners at University

Nychkalo

Jin-ba

Lukianova

et al. 2020

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“…As seen earlier, it is noted that other types of questions and practices can be well employed in assessing the English language (Khansir, 2018), (Frost, 2005), (Arif et al, 2021, (Sychev et al, 2020), (Alenezi, 2020), (Jennach, 2021.…”

Section: Problem Of the Studymentioning

confidence: 99%

“…It is well known that not only multiple-choice but also other types of questions and practices can be well employed in assessing language, e.g. open-ended questions (Sychev et al, 2020), error correction (Khansir, 2018), true or false (Frost, 2005), portfolio-assessment (Arif et al, 2021), essay/ text writing questions (Sychev et al, 2020), as well as translationperformance/task-based translation questions (Alenezi, 2020), language situation ones/dialogues assessing language functions (Jennach et al, 2021), to maintain and cover all which could be included in an English exam type wise).…”

Section: Challenges To Changementioning

confidence: 99%

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Evaluating 2020-2021 and 2021-2022 GCSE English Exams and Exam Method against Related Curriculum Goals and Stakeholders’ and Experts’ Perceptions

Albaaly¹

2022

IJELLR

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Evaluation instigates learning and teaching and is central to them. All elements in the educational process have to be evaluated, including the evaluation of exams and related exam methods. This study aimed at evaluating the Egyptian GCSE English exam and exam method against GCSE curriculum goals and stakeholders’ (i.e. teachers’ and students’) perceptions of the exams. The study adopted a qualitative approach: a fixed alternative questionnaire and a semi-structured E-interview both administered to participant experts and practitioner teachers (n 50 and 5, respectively), and it also incorporated designing and using another questionnaire (in two versions) administered to GCSE graduates: 50 for the school year 2020-2021 and 100 for the 2021-2022. Results revealed that certain curriculum language skills and elements were absent, and the vast majority of them were under-represented and improperly addressed. Even, the curriculum novel content was covered inappropriately. It was then found out that the exams had few strengths and many weaknesses, regarding the question type, language skill and language element representation/coverage, exam nature and appropriateness for students, incompliance with curriculum and OBD. Other exam implications were covered. Plans and recommendations for intervention included inclusion of performance-based questions for three language skills and the translation element, coverage of grammar and vocabulary, inclusion of language functions and idioms, variation of question types appropriate for language skill/element, and commitment to the OBE approach in the exams as well as to research.

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“…The closed, passive, isolated, and spoon-fed traditional teaching mode of college English translation obviously cannot meet the needs of today's society for college English teaching [3][4][5]. DDL (data-driven learning) advocates the deep integration of modern information technology and resources based on corpus and search engine with education and teaching, which opens up a new path for college English translation teaching [6].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Teaching Model Design of College English Translation Using Intelligent Processing Technology

Zhang

2022

Wireless Communications and Mobile Computing

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DDL (data-driven learning) advocates a deep integration of modern information technology and resources based on corpora and search engines with education and teaching, opening up a new path for college English translation instruction. The concept and characteristics of DDL theory are discussed in this paper. This system proposes an automatic scoring model of English translation based on mixed vector space, which comprehensively analyzes the target translation from two aspects: grammatical errors and semantic relevance, in order to address the shortcomings of current algorithms. The findings of this study show that using a combination of English fragmentation translation and adaptive resource push, learners can learn English reading using mobile terminals in fragmented learning situations, as well as receive targeted training for weak links in English reading in fragmented learning time and space, improving their learning efficiency and effect.

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Task-based Approach in Teaching Translation: A Case Study in Jouf University

Cited by 12 publications

References 11 publications

Use of Task-Based Approach in Teaching Vocabulary to Business English Learners at University

Use of Task-Based Approach in Teaching Vocabulary to Business English Learners at University

Evaluating 2020-2021 and 2021-2022 GCSE English Exams and Exam Method against Related Curriculum Goals and Stakeholders’ and Experts’ Perceptions

Data-Driven Teaching Model Design of College English Translation Using Intelligent Processing Technology

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