An Action Research on Primary School Students’ Ability to Narrate Chinese Stories in a Foreign Language with School-Based Micro-Course
Bibliographic record
Abstract
To explore how to develop primary school students’ ability to narrate Chinese stories in foreign language, in this study we employ two rounds of teaching action through school-based English micro-courses using a flipped classroom. Through a quantitative analysis of the grades of the 163 students in the pretest and post-test using SPSS 27.0 and a qualitative analysis of the interviews with the four teachers and 163 students using Nvivo12 after the action, we find that students’ ability to narrate Chinese stories in foreign language improve significantly. Specifically, the grades of the students in the vocabulary test increase markedly, but the scores in the cross-cultural communication competence section improve, but not significantly. However, the reader can discern progress in the cognitive, affective, and operational aspect of cross-cultural communication competence through the student interviews.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".