Explicit and implicit teaching Persian language structures and editing methods and translation performance of Iranian undergraduate translation students

Abstract

Purpose The purpose of this paper (experimental–comparative research) is to investigate the possible impacts of explicit and implicit teaching Persian structures and editing methods on the translation performance of the Iranian undergraduate translation students. Design/methodology/approach This research enjoyed a quasi-experimental design. A quasi-experimental research design was used in this research, as it was impossible to assign random sampling to the subjects. In addition, this research was a comparative group study as there were two experimental groups with two different treatments and one control group with placebo. Table I represents the design of the research. Findings The results showed that before the treatment there were no significant differences between three groups in terms of translation performance; however, after treatment, the results indicated a statistically significant difference between two experimental groups and treatment group. Moreover, explicit instruction yielded more positive results than the implicit group. Originality/value Although research in the field of translation assessment and quality in relation to target language are prevalent and in spite of the abundance of research in the field of implicit/explicit instructions in second language teaching and learning, there is no research (to the best knowledge of authors) which looks at translation performance from teaching structures and editing methods of target language perspective with the focus of explicit and implicit (in an English–Persian context).

Dynamicity of interaction in academic discourse: Evidence from a corpus-based study

“…Unlike the commonplace method of detecting and quantifying language features manually which is not only time-consuming but also subject to human error (Heng and Tan, 2010), one reliable way is to utilize corpora which are among the most versatile tools for analyzing a large ensemble of texts stored electronically (Anderman and Rogers, 2008;Vasheghani Farahani and Pahlevanzade Fini, 2023;Zanettin et al, 2003). Indeed, corpora make it plausible to look for specific language features in a large number of texts in a systematic way (Candel-Mora and Vargas-Sierra, 2013).…”