Abstract
AbstractThe current study examined the extent to which cognitive fluency (CF) contributes to utterance fluency (UF) at the level of constructs. A total of 128 Japanese-speaking learners of English completed four speaking tasks—argumentative task, picture narrative task, reading-to-speaking task, and reading-while-listening-to-speaking task—and a battery of linguistic knowledge tests, capturing vocabulary size, lexical retrieval speed, sentence construction skills, grammaticality judgments, and articulatory speed. Their speaking performance was analyzed in terms of speed, breakdown, and repair fluency (i.e., UF), and scores on linguistic knowledge tests were used to assess students’ L2 linguistic resources and processing skills (i.e., CF). Structural equation modeling revealed a complex interplay between the multidimensionality of CF and UF and speaking task types. L2 processing speed consistently contributed to all aspects of UF across speaking tasks, whereas the role of linguistic resources in speed and repair fluency varied, depending on task characteristics.
Highlights
Oral fluency is one of the most robust indicators of second language (L2) proficiency (Tavakoli et al, 2020)
The latent variable of linguistic resource consisted of cognitive fluency (CF) measures capturing the range of linguistic resources, whereas the latent variable of processing speed was composed of reaction time (RT)-based measures and the articulatory speed measure
Our research has demonstrated that the construct of CF consists of two dimensions—linguistic resource and processing speed—and confirmed the robustness of Tavakoli and Skehan’s (2005) three-dimensional model of utterance fluency (UF) across tasks
Summary
Oral fluency is one of the most robust indicators of second language (L2) proficiency (Tavakoli et al, 2020). In the context of learning, teaching, and assessment of L2 speaking skills, oral fluency is commonly regarded as one of the major learning goals. For a better understanding of L2 fluency as a construct and as an important language learning target, it is essential to examine how underlying linguistic knowledge contributes to students’ fluent speech production. Insights into how L2 users’ linguistic resources and processing mechanisms contribute to the efficiency of speech production may assist language teachers and materials designers and inform language teaching policy makers what linguistic knowledge areas and skills to develop so that L2 learners may become fluent speakers.
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