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.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".