Marty Meinardi

Speed bumps for authentic listening material

This article investigates whether authentic native speaker (NS) to NS speech can be made available to the learner listener through the use of a novel slow-down tool. Results from various preliminary tests seem to indicate that the use of a slow-down algorithm in many cases, and in particular in samples with a higher speed rate and word count, leads to an improvement in subjects’ ability to perceive and understand what was being uttered in the samples. Tests revealed that even NS listeners, as opposed to non-native (NN) listeners, prefer to hear authentic NS speech which is either unscripted or is influenced by regional accent, at a slowed down speed. It also seems that ‘unexpected’ words (such as words with high contextual value, but which cannot be processed in a top-down fashion because of the size of the sound snippet) are initially not understood at the original speed of delivery, even in a scripted and carefully pronounced pedagogic sample. Samples containing chunks or formulaic sequences, however, appear to be easily understood at 100% by the majority of NS listeners due to the holistic processing of these language units.

Shared and Mutual Knowledge in Language Learning

This article discusses the matter of shared knowledge from the perspective of the different deictic centres of understanding. Difficulties, which may result from a possible lack of a shared knowledge between NSs and NNSs due to differences in language experiences and personal backgrounds, are identified. Word count: 45 Shared and Mutual Knowledge in Language Learning

Assessing the Writing of International Learners: a Discussion in Two Voices

This Article is brought to you for free and open access by the School ofLanguages at ARROW@DIT. It has been accepted for inclusion in Articlesby an authorized administrator of ARROW@DIT. For more information,please contactyvonne.desmond@dit.ie, arrow.admin@dit.ie.This work is licensed under aCreative Commons Attribution-Noncommercial-Share Alike 3.0 License