Conhecimentos Prévios sobre Meios Digitais e Desempenho no Ensino Remoto Durante a Pandemia COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it