Shifting Sands in Second Language Pronunciation Teaching and Assessment Research and Practice

Abstract

This article brings to the fore trends in second language (L2) pronunciation research, teaching, and assessment by highlighting the ways in which pronunciation instructional priorities and assessment targets have shifted over time, social dimensions that, although presented in a different guise, appear to have remained static, and principles in need of clearer conceptualization. The reorientation of the pedagogical goal in pronunciation teaching from the traditional focus on accent reduction to the more suitable goal of intelligibility will feed into a discussion of major constructs subsumed under the umbrella term of "pronunciation." We discuss theoretical gaps, definitional quagmires, and challenges in operationalizing major constructs in assessment instruments, with an emphasis on research findings on which pronunciation features are most consequential for intelligibility and implications for instructional priorities and assessment targets. Considerations related to social judgments of pronunciation, accent familiarity effects, the growth lingua franca communication, and technological advances, including machine scoring of pronunciation, pervade the discussion, bridging past and present. Recommendations for advancing an ambitious research agenda are proposed to disassociate pronunciation assessment from the neglect of the past, secure its presence as an integral part of the L2 speaking construct, and propel it to the forefront of developments in assessment. L2 pronunciation assessment 3 Shifting sands in second language pronunciation assessment research and practice From a historical perspective, it can be argued that pronunciation, more than any other component within the construct of second language (L2) speaking ability, has been subject to the whims

A comparison of holistic, analytic, and part marking models in speaking assessment

“…The wide use of automated scoring technologies also offers the potential for different hybrid models of machine and human scoring (Isaacs, 2018; de Jong, 2018). For example, an auto-marker can focus on features of speech that it can most reliably assess, such as fluency and pronunciation, while raters can focus on more complex elements such as coherence or task achievement.…”

How technology affects instruction for English learners

“…For example, speech-recognition software, which detects speech and assesses the speaker’s pronunciation, grammar, and/or diction, is popular with language teachers and students because it can help them improve their spoken intelligibility or comprehensibility (though evidence suggests it is a more effective tool for teaching pronunciation than other language skills; Derwing, Munro, & Carbonaro, 2000; Issacs, 2018). Further, it provides real-time feedback in a semiprivate environment (Ahn & Lee, 2016), which can shield language learners from scrutiny, allowing them to practice their English without fear that they’ll be mocked or bullied by native speakers (Neri, Cucchiarini, & Strik, 2003).…”

L’apprentissage des correspondances phonème -graphème en français langue étrangère

“…Deuxièmement, le travail de certaines habiletés telles que la prononciation a été totalement négligé ces trente dernières années car elles semblaient ne pas être utiles au processus communicatif. Or, de nombreux chercheurs ont montré combien la prononciation est essentielle à l'apprentissage de la L2 (Isaacs, 2018 2018). Les résultats de la présente étude appellent à une évaluation des effets d'une intervention pédagogique centrée sur l'acquisition des CPG pour les phonèmes susceptibles d'être au coeur des interférences cross-linguistiques lors de la perception et de l'écriture de ces phonèmes en L2.…”