Feedback in the Very Young Learner EFL Classroom: A Review Study
Bibliographic record
Abstract
This review article contributes to addressing an urgent issue in today's globalised world. English has become the world lingua franca and many countries are seeking to equip their workforce with English competence so as to engage fully in international social, economic and academic fora. This often entails lowering the age at which children start to learn English in school, which places a heavy responsibility on the educational system in countries, such as Saudi Arabia, where English is a foreign language, i.e., not widely used in day-to-day life in the country outside the English classroom. Yet many aspects of teaching young learners a foreign language at age six onwards have not been extensively investigated. One of these is the key issue of how the teacher should best give feedback to children of that age. Feedback is widely regarded as the key to learning, but not all its types are necessarily effective. Furthermore, most research has been on older children and adults. The present study therefore uses the method of qualitative systematic literature review to assemble and analyse existing research and theory with the aim to extract guidelines for feedback by teachers in foreign language classrooms with very young learners. It is found that attention should be given not only to negative, corrective feedback on the language used, which is often the focus of teacher attention. A case is made for the place and value of positive feedback and feedback of communicative and emotional types. Even in corrective feedback it is often better to use partial rather than total correction, so as to give the child space to self-correct.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".