Teaching Practicum During the Covid-19 Pandemic: A Comparison of the Practices in Different Countries
Bibliographic record
Abstract
Today, many countries ensure that student teachers get into the real classrooms, practice in there, spend more time and translate theoretical knowledge into practice in schools during Initial Teacher Education. So that they can receive stronger support in the practicum process, and they can develop themselves. However, schools have been closed in so many countries due to the Covid-19 pandemic preventions. Therefore, countries have rearranged the teaching practicum process. The aim of this study, which was carried out with a systematic review, is to comparatively examine the teaching practicum processes of different countries during the Covid-19 pandemic. With a systematic review made according to certain criteria, teaching practicum in the Covid-19 in the countries of Australia, Canada (Ontario State), England, Greece, Hong Kong, Malaysia, Portugal, South Africa, Turkey, the United States of America (New York State) and Zimbabwe were examined. According to the findings, it has been seen that some countries have removed or stretched the teaching practicum requirement during the Covid-19, while some countries have carried out online teaching practicum (i) in K-12 schools, (ii) with peer learning, or (iii) using VR technology, and one country re-opened the schools after a short closure.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".