Canadian second language teachers’ technology use following the COVID-19 pandemic.
Bibliographic record
Abstract
If teachers have previously used technology (e.g., Learning Management Systems, document sharing, video conferencing, gamification, social media or video recording), they are likely to use it again. For second language (L2) teachers, sudden or planned for online instruction during the COVID-19 pandemic may have resulted in their using new or familiar technology to support their pedagogy, engage students, or provide authentic target language input. However, since online instruction was temporary, perhaps their use of certain technologies was temporary as well. To investigate L2 teachers’ use of technology before, during and (anticipatedly) post-pandemic, we statistically analyzed data on technology use (n=18 items) from a survey of Canadian L2 teachers (n=203). We inquired about their use of Learning Management Systems, document sharing, video conferencing, gamification, social media, and video recording. Our findings revealed that teachers’ use of technology during the pandemic predicted their anticipated use post-pandemic. Teachers who used any of the six technologies during the pandemic were significantly more likely to anticipate using those same ones post-pandemic than those who did not. Despite the challenges of implementing these tools under these circumstances, these six technologies may remain as part of L2 teaching moving forward.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".