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 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) after the pandemic, this study statistically analyzed data on technology use ( n = 18 items) from a survey of Canadian L2 teachers ( n = 203). It inquired about their use of Learning Management Systems, document sharing, video-conferencing, gamification, social media, and video-recording. Findings reveal 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 in the future.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".