The Validity and Reliability of Rhythm Measurements in Automatically Scoring the English Rhythm Proficiency of Chinese EFL Learners
Bibliographic record
Abstract
This article aims to find out the validity of rhythm measurements to capture the rhythmic features of Chinese English. Besides, the reliability of the valid rhythm measurements applied in automatically scoring the English rhythm proficiency of Chinese EFL learners is also explored. Thus, two experiments were carried out. First, thirty students of English major and five native English speakers were selected to read ten English sentences. The participants were divided into four proficiency groups according to human scoring. Then seven previously proposed rhythm measurements were investigated in four proficiency groups. One-way ANOVA results showed that five rhythm measurements were valid to distinguish different English rhythm patterns among four proficiency groups. Based on the valid measurements, an experiment of automatic scoring for English rhythm proficiency was also conducted through statistical technique Multiple Regression. The correlation coefficient between the autoscores and the scores made by experienced teachers reached 0.866. The result showed a high reliability of the objective evaluation for English rhythm proficiency of Chinese EFL learners.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".