Learning through drama to build social confidence: a phenomenological study of the experiences of six Chinese- Canadian children in a public speaking class
Bibliographic record
Abstract
This study explores the extent to which learning through drama might help to build social confidence in Chinese-Canadian children. Guided by the principle of Interpretive Phenomenology, I use Portraiture as my main methodology, using observations, interviews and field notes. Through the use of drama, I taught Public Speaking to a class of six Chinese-Canadian children, aged seven to eight, in Montreal, over the course of six months. I placed emphasis on the use of two drama plays, the sharing parts in the plays, and supported my findings with interviews with parents. I made portraits of these six children according to my observations, class performances, and peer evaluation. Through analyzing the data I came up with four areas that these children exemplified that constitute social confidence. They are sociability-cooperation, shyness-sensitivity, peer-acceptance and rejection, self-perception of self-worth. The results show that these children have greatly improved in these four social skills, which suggests that learning through drama did help them to build social confidence. Additionally, I discussed the difference between learning through drama and drama education. And I outlined the practical implications of learning though drama, which might have implications for working different ethnic groups in Canada. Key words: learning though drama, social confidence, Chinese-Canadian children
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".