Strategic Competence and Foreign Language test Performance in Iranian Context

International Journal of Foreign Language Teaching and Research

مقاله 1، دوره 1، شماره 3، آذر 2013، صفحه 11-21   

نوع مقاله: Research Papers

نویسندگان

Siamak Rahimi¹؛ Behzad Ghonsooly^{* 2}

¹Ph.D. Candidate, English Department, Ferdowsi University of Mashhad

²Professor in Applied Linguistics, Department of English, Ferdowsi University of Mashhad

چکیده

A number of studies have accounted the integral role of foreign/second language learning and learner strategy use. However, a few of these studies have considered the relationships between strategic competence and its use and foreign language performance (FLP). This study applied structural equation modeling to deeply investigate the relationships between test takers’ strategy use and their performance on foreign language tests. For this purpose, 147 EFL learners from three well-known branches of a private language institute in Tehran, Iran were chosen. These participants also responded to an 80-item Cognitive and Metacognitive strategy questionnaire and took an 81-item Key English Test (KET). Consequently the hypothesized model of strategic competence vis-à-vis FL test appeared with following findings. First, metacognitive processing (MP) had no direct effect on FLP but a significant, direct influence on cognitive processing (CP), which supports the interaction notion of Bachman and Palmer’s (1996) communicative competence . Second, due to the Iranian EFL context in which Iranian students and teachers show more tendencies toward memorizing vocabulary and analyzing grammar, practicing naturalistically was insignificant and deleted while the translating variable was added, which was highly significant. Third, CP had no significant effect on listening ability which shows that listening ability is much more dependent on contextual factors rather than background knowledge and strategic competence.

کلیدواژه‌ها

strategic competence؛ cognitive processing؛ metacognitive processing؛ foreign language performance؛ structural equation modeling