Teacher efficacy for online teaching during the COVID-19 pandemic
Bibliographic record
Abstract
The purpose of this study was to examine secondary teachers’ efficacy for teaching in a fully online teaching environment during the sudden transition to online teaching that happened due to the COVID-19 pandemic. This study was aimed at understanding how specific variables, teaching experience, professional development (PD) experience, and teaching supports might correlate with self-efficacy perceptions of teachers transitioning to online teaching during a pandemic in the domains of student engagement, instructional strategies, classroom management and computer skills. The instrument used to measure teacher efficacy for online teaching was a web based 32-item survey that was given to Ontario secondary teachers in a greater Toronto district school board. We argued that prior experience with online learning such as Additional Qualification (AQ) courses or online professional development would build greater self-efficacy amongst teachers as they transition to online learning. The results indicated that higher online teaching efficacy scores correlated with having taken online Additional Qualification (AQ) courses[1] and online professional development sessions. The highest online teaching efficacy scores correlated with having previously used the board provided learning management system (LMS) and using virtual technology supports. These indicators are correlated with higher scores of online teaching efficacy but require further investigation as to how they can better provide support for teachers in online learning environments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".