The use of play in English as a foreign language classrooms: Chinese teachers’ perspectives
Bibliographic record
Abstract
This semi-structured interview study seeks to describe Chinese teachers’ understanding and concerns about the use of play in supporting young children’s English learning. Eight English as a foreign language (EFL) teachers of children aged between 3 to 8 years were involved, including both local teachers and international teachers. Findings reveal that because of the unique feature of EFL learning, structured play-based learning was mainly discussed and favored by teachers. Although teachers showed an inclination of separating learning from play, they believed that play serves a supporting role in maintaining students’ interest and motivation in English learning, which is especially crucial important for young children. However, teachers are concerned that play’s benefits would be considered being neutralized when children get older from parents’ perspectives which will eventually negatively impact teachers’ teaching practices. Teachers also face the barriers of balancing the gap between child’s language abilities and cognitive development in EFL teaching. The findings and discussions raise implications for researchers and practitioners to rethink how to define play. A broader definition of play-based learning will help balance both the needs for academic teaching and the benefits of incorporating play, as well as provide implications for integrating play pedagogy in public school systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".