At low tide… or what teachers took away from their distance education experience
Bibliographic record
Abstract
During the first months of the pandemic, TÉLUQ University played a major role in supporting all teachers in Quebec (and beyond) by creating the J’enseigne à distance (I teach at a distance) training programme. This programme, created in four months, includes four microprograms (support, disseminate, adapt and evaluate) for the different sectors of education. The modules were put online as each was created and have been consulted by more than 300,000 people. Now that in-person teaching has resumed for over a year, to what extent does this training still have an impact on teaching practices? We propose to reflect on this topic in light of the results of an April 2023 survey sent to people who participated in the programme. Although the majority of them now teach in person, more than half of them indicate that they use what they learned during the training sometimes or often, or even daily. Most of the respondents report having modified their teaching resources, learning activities or teaching and coaching methods since the pandemic. Thus, the transition to distance learning seems to have promoted certain changes in practices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".