The Hard Teacher’s Leadership Coping to the COVID-19 Pandemic
Bibliographic record
Abstract
Most teachers in Mexico are not experts on Information and Communication Technologies, some rural areas lack a good internet connectivity or even electricity. This context led us to determine: How can teachers keep the pace of educational leadership? and How they cope their teaching task with the COVID-19 pandemic? The sample included 329 teachers from urban and rural zones, 71.1% female and 28.9% male, with a mean age of 38.8 years, working in public (71.7%) and private (28.3%) schools. A self-evaluation template was used to assess the planning, didactical sequence analysis and evaluation competence from the teachers. Our aim was to sketch a teacher’s leadership competences profile, specifically in these pandemic times. The results showed than 75.7% of the teachers had an internet access between Good and Very good; on the contrary, 78.4% of the teachers considered that most of their students had between “not very good” to “very bad” internet access. Only a few teachers addressed the didactic planning or followed its development and assessment: I have elaborated and shared with the students indicators of achievement from the didactical sequence (32.8%); I have stimulated processes of reflection upon learning through an instrument (22.5%); I have regularly incorporated and used digital tools and Internet (31.9%); at last, I have established and conducted moments of evaluation, self and formative co-evaluation in which the students have been able to make changes based on the feedback received (30.1%). However, teachers are coping with this pandemic time and it may involve a change in educational strategies towards the future.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".