Archive

Posts Tagged ‘timestretching’

Presentation on Time-stretched Audio and Personalized Provision in Instructor-led Digital Audio Labs @ Nerallt/Neallt 2009, Yale University, New Haven, CT

The pervasiveness of networked digital media – new delivery forms for digital TV and radio by the traditional media industry, as well as new content providers using pod- and tube-casts -, owing to an ever more powerful, robust and – partially as an overhang of the bubble – abundant technical hard- and software infrastructure, has also revitalized – and poured substantial new resources into the modernization of – the older concept of the language lab. Computerized classrooms with network and multimedia facilities, basic classroom management systems and centralized databases, with some interfacing to serve as learning material repositories or portfolios demonstrating learning outcomes, have become a common underlying fabric for many of the constituents’ learning environments. The recent freezing up of the resource flow can serve as a wakeup call to remind us both of the critical “What is the benefit, or return on investment?” and of the original promise of e-learning: increased efficiency. On the one hand, scaling through crowd-sourced or automated sourcing and reuse of materials has become a pressing need in rapidly expanding second language programs like English and Spanish that new technologies can help meet. On the other hand, widely differing learner proficiency is increasingly a problem when trying to form classes in the shrinking programs of other languages, and personalization of learning provision is increasingly expected in an environment shaped by “long tail”-economies. This paper will evaluate common practices in SLA that have served as workaround, recapitulate a number of different time-stretching algorithms, summarize existing software solutions and introduce a new option which is based on MS-Windows Media Encoder’s time-stretching and pause detection capabilities. Finally, the presentation will exemplify instructor-led utilization of this simplified and/or automated time-stretching of authentic materials, with more teacher-control and a more realistic output than that built into current media players, as a – not exclusive, but valuable – step towards more comprehensible input of level “i+1” in a more personalized language learning provision.

Slide Deck: plagwitz_timestretching_audio_nerallt09.pdf

Pedagogical rationale of timestretching audio for differentiating instruction

  1. Context: Higher Education in the UK has made considerable investments in digital lab infrastructure to improve second language instruction in times of deteriorating language take-up in the secondary sector, including widening participation. Digital language labs, apart from generic digital media, suffer from a lack of custom-made teaching materials that take advantage of the pedagogic features of the lab: grouping for personalization of teaching and learning. Pedagogical integration and development is needed to achieve the original intentions. A project to timestretch audio language learning materials for the digital audio lab promises integration software, pedagogical materials and, above all, a model of effective digital language lab use in teaching.
  2. Problem: In times of uneven language provision at the secondary school level and of shrinking language program sizes in HE, increasingly language teachers find themselves confronted with uneven language proficiency in their courses. Digital lab technology can help them to overcome the  “one size fits all” approach and personalize the students learning experience, for a greater inclusiveness in language programs and an increased proficiency boost for both the below and above average proficiency student groups.
  3. During my work with the language programmes at an English university, I could witness – and had to record – that the least proficient students, seeing themselves confronted with what was nowhere near “comprehensible input” (Krashen) for them, not only let the communication break down, but appeared so distressed that, despite being fully aware that their language output was being recorded as an assessment for the teacher to evaluate, started to curse and swear (in their native tongue) – while at the same time the upper portion of the class breezed through the exercise without any apparent difficulty.
  4. Proposed Solution:
    1. Technology to the rescue: The slowing down of digital audio – without pitch alteration –has been, while not a perfectly accurate representation of natural slow speech output, a popular benefit of digital technology in the language learning field for several years now (cf. e.g. Calico 2004), and I myself have experimented with it in the digital audio lab (Model imitation and Question – response exercises) and in publications (cf. Plagwitz, Karaoke in the Digital Audio Lab (2006)).
    2. What seems lacking are
      1. both an application that automates (by monitoring one of the network share directories that are part of the digital lab system) the slowing down (and speeding up) of audio for instructors (e.g. in 5% increments from 70% to 120% of original input) that are too time-pressed for producing materials, or even seeking out recordable on-air sources, and
      2. a model implementation in the digital audio lab (using dynamic grouping of students through the digital lab software) that creates exercises that would create exercises that can benefit from this approach (and can be shared), that applies them in a number of suitable (interpreting, ab initio language learning) modules and that assesses the proficiency improvement with this approach (using the outcome exam and a control group).
  5. Benefits: Greater fluency of both the least and most proficient students is to be expect after they were exposed to – as deemed fit by their instructors – slowed down/sped up exercises – ca. 20 exercises in the ab initio language learning module, practicing a small set of suitable new structures and vocabulary compared, with 2 control groups, and five interpreting rounds of 20-30 minutes. We will operationalize this by reusing regular assignment grading and use a control group, also of module-size, which must also use the digital audio lab, but with “one size fits all” audio.