Technology-Mediated Workplace Language Training: Developing and Assessing a Module for a Blended Curriculum for Newcomers
Bibliographic record
Abstract
Canadian newcomers with low proficiency in English or French face challenges in the workforce (Kustec, 2012).While language classes provide workplace language training, some newcomers are unable to attend face-to-face classes (Shaffir & Satzewich, 2010), suggesting a need for outside-the-classroom occupation-specific language training.Technology has been advantageous for second language learning outside the classroom (Stockwell, 2007), with mobile-assisted language learning affording learners greater control over their learning (Yang, 2013).As part of a larger project, this thesis presents the development and assessment of a technology-mediated task-based module providing workplace-linguistic support on mobile devices, enabling learners to access language instruction when and as needed.The modules were assessed by newcomers of high-beginner English proficiency employed in customer service (n=4) and their teachers (n=4).Results confirm benefits of the approach in providing instruction meeting participant needs, ensuring opportunities for individualized training.Implications for designing and implementing technologymediated modules are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".