Conhecimentos Prévios sobre Meios Digitais e Desempenho no Ensino Remoto Durante a Pandemia COVID-19
Bibliographic record
Abstract
As of the 1st quarter of 2020, the World Health Organization (WHO) declared the pandemic caused by the Sars-Cov-2 virus, infectious agent of COVID 19, social distancing was proposed as means to deal with this emergency and teaching went remote. In order to find out how teachers dealt with this situation, we conducted a survey using an online questionnaire and asked them to answer, among other aspects, their familiarity with digital media, their perception of their students' appreciation to this type of class and how much of what they have learned during the pandemic they will take to their classrooms once we return to face to face classrooms. In conclusion the technological advances available are allowing teachers, students and guardians to achieve the necessary educational goals, however, they do not guarantee the desired equity. The mismatch of technological advances between teachers and students, between city regions, between social and economic power etc., reveals the delicate situation of the educational system in our state. Even in schools and public universities where several strategies have been taken to give students more accessibility, it is still not possible to guarantee it. One of the obstacles beyond the economic one is the preparation of teachers who have not advanced to some of the needs of the 21st century. Keywords: COVID-19. Remote learning. Digital information and communication technology.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".