Helping Language Teachers in Lithuania take their First Action Research Steps During the COVID-19 Pandemic: An Online Strategy
Bibliographic record
Abstract
This paper reports on the experiences of the authors teaching action research workshops as professional development for language teachers in Europe during the Covid-19 pandemic. It describes work carried out for Action Research Communities for Language Teachers, which is funded under the Training and Consultancies programme of the European Centre for Modern Languages of the Council of Europe as part of its aim to promote quality language education in Europe. The paper focuses on the necessary pivot from face-to-face to online action research workshops and project development in a difficult global context for a group of teachers in Lithuania. It outlines the challenges experienced by the authors and teacher participants, the lessons learned in online teaching of action research, and the positive outcomes for language teachers in setting out on their action research journeys. The paper contributes to the literature on action research in language education and professional development during Covid-19.
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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 | 0.037 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.044 |
| Research integrity | 0.007 | 0.007 |
| 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".