Teacher’s Sense of Efficacy During a Time of Crisis
Bibliographic record
Abstract
Vocational education and training (VET) or technology education (TE) has the creating, making, and doing aspects of human activity at its foundation. This article presents a comparison of the teaching sense of efficacy (TSE) of practicing VET teachers and teacher candidates (TC) during a forced switch to emergency remote teaching (ERT). In Phase 1, the switch to ERT had a significantly negative effect on VET teachers (N = 42; r = -.60). In Phase 2, VET TCs (N = 16) were similarly affected (r = -.53). Results of a two-way mixed ANOVA in Phase 3 suggest that ERT had a greater negative impact on practicing VET teachers’ TSE for student engagement (partial eta squared = .11) and classroom management (partial eta squared = .19) than it did on VET TCs’ TSE. As novice teachers tend to draw more from contextual factors than mastery experiences, this research suggests that experienced teachers were at a greater loss due to the pandemic than TCs.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".