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<p>Cross-linguistic awareness has gained increasing attention due its impact on language learning process. This study investigated the predictive role of Cross-Linguistic Awareness (CLA) and its components in shaping language learning motivation among Iranian learners of English as a Foreign Language (EFL). Drawing on a sample of Iranian EFL learners (<i>N</i> = 148) and employing Bayesian statistical methods, the study examined the strength and nature of associations between CLA dimensions—segmental, suprasegmental, morphological, semantic-pragmatic, affective, and cultural—and learners’ motivation. Descriptive statistics revealed variability across CLA components, with the affective and semantic-pragmatic dimensions scoring highest. Bayesian correlation analysis indicated a moderate positive relationship between overall CLA and motivation while model comparison revealed the suprasegmental and semantic-pragmatic components as the strongest predictors of motivation, explaining up to 21.5% of its variance. Overall CLA also emerged as a significant predictor, accounting for 17.2% of motivational variance. These findings underscore the importance of fostering cross-linguistic sensitivity—particularly in prosodic and pragmatic domains—to enhance learners’ motivation in EFL contexts.</p>

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Cross-Linguistic Awareness as a predictor of language learning motivation: a bayesian perspective

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月4日

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  • Motahareh Saadati,
  • Seyed Hassan Talebi

摘要

Cross-linguistic awareness has gained increasing attention due its impact on language learning process. This study investigated the predictive role of Cross-Linguistic Awareness (CLA) and its components in shaping language learning motivation among Iranian learners of English as a Foreign Language (EFL). Drawing on a sample of Iranian EFL learners (N = 148) and employing Bayesian statistical methods, the study examined the strength and nature of associations between CLA dimensions—segmental, suprasegmental, morphological, semantic-pragmatic, affective, and cultural—and learners’ motivation. Descriptive statistics revealed variability across CLA components, with the affective and semantic-pragmatic dimensions scoring highest. Bayesian correlation analysis indicated a moderate positive relationship between overall CLA and motivation while model comparison revealed the suprasegmental and semantic-pragmatic components as the strongest predictors of motivation, explaining up to 21.5% of its variance. Overall CLA also emerged as a significant predictor, accounting for 17.2% of motivational variance. These findings underscore the importance of fostering cross-linguistic sensitivity—particularly in prosodic and pragmatic domains—to enhance learners’ motivation in EFL contexts.