Technology-Mediated Language Training: Developing and Assessing a Module for a Blended Curriculum for Newcomers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Newcomers to Canada with low proficiency in English or French often face challenges in the workforce (Kustec, 2012). While language classes provide workplace language training, not all newcomers are able to attend face-to-face classes (Shaffir & Satzewich 2010), suggesting a need for outside the classroom, occupation-specific language training. The use of technology has been shown to be advantageous for second language (L2) learning (Stockwell, 2007), especially when used outside the classroom (i.e., mobile-assisted language learning), as mobile technology affords learners greater control and flexibility over their own learning (Yang, 2013). This paper reports on a study investigating the development of a blended curriculum for L2 learners employed in customer service. A technology-mediated module was designed and developed within a task-based language teaching framework to provide workplace-linguistic support on mobile devices, enabling learners to access the language instruction they needed, when they needed it. The module contents and usability were assessed by high-beginner English proficiency newcomers employed in customer service (n=4) and their volunteer teachers (n=4). Results confirm the overall benefits of using language learning technology in providing instruction that meets participant language needs, ensuring opportunities for individualized training. Implications for designing, implementing, and researching technology-mediated modules are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it