Using Blended Learning as an Innovative Delivery Model for an In-House Language Program
Bibliographic record
Abstract
This paper reports on the development and implementation in 2012 of McGill University’s French at Work program for McGill employees, using a blended learning model. The program is an example of how a reduction in face-to-face teaching presents one solution to employees’ scheduling constraints and how this model might offer suggestions for the development of similar programs in a higher education setting.McGill University’s French at Work program welcomes a diverse participant body from different faculties and service groups. Created in response to decreasing enrolment and higher levels of absenteeism, the authors report that interdepartmental collaboration and a complete course redesign, along pre- established professional themes, were required in its development. Using the University’s Learning Management System (Desire2Learn) the course incorporates in-class sessions, self-directed, web-based activities as well as synchronous and asynchronous online discus- sions employing Microsoft Lync.A subsequent increase in registration and retention rates strongly suggests the program answers a real need for professional development in French as a second language within McGill University through innovative use of certain technologies.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".