Accessible Immersion Metrics (AIM) for Second Language Acquisition : A Constructivist Innovation for the ESL Classroom

Abstract: This study was designed to validate a constructivist learning framework, herein referred to as Accessible Immersion Metrics (AIM), for second language acquisition (SLA) as well as to compare two delivery methods of the same framework. The AIM framework was originally developed in 2009 and is proposed as a “How to” guide for the application of constructivist learning principles to the second language classroom. Piloted in 2010 at Champlain College St-Lambert, the AIM model allows for language learning to occur, free of a fixed schedule, to be socially constructive through the use of task-based assessments and relevant to the learner’s life experience by focusing on the students’ needs rather than on course content.||Résumé : Cette étude a été principalement conçu pour valider un cadre d'apprentissage constructiviste, ci-après dénommé Accessible Immersion Metrics - AIM, pour l'acquisition d'une langue seconde - SLA. Le cadre de l'AIM est proposé comme un mode d'emploi pour l'application des principes constructivistes à l'apprentissage d’une langue seconde. Créé en 2009 par l'auteur, et piloté en 2010 au Collège Champlain St-Lambert, le modèle de l'AIM permet l'apprentissage des langues à se produire, sans horaire fixe et socialement constructive grâce à l'utilisation des évaluations alignées basées sur des tâches pertinentes à l'expérience de vie de l'étudiant en se concentrant sur les besoins des élèves plutôt que sur le contenu des cours.

A flexible bio-inspired hierarchical model for analyzing musical timbre

A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.