An investigation into native and non-native teachers' judgments of oral English performance: A mixed methods approach
Bibliographic record
Abstract
This study used a mixed methods research approach to examine how native English-speaking (NS) and non-native English-speaking (NNS) teachers assess students' oral English performance. The evaluation behaviors of two groups of teachers (12 Canadian NS teachers and 12 Korean NNS teachers) were compared with regard to internal consistency, severity, and evaluation criteria. Results of a Many-faceted Rasch Measurement analysis showed that most of the NS and NNS teachers maintained acceptable levels of internal consistency, with only one or two inconsistent raters in each group. The two groups of teachers also exhibited similar severity patterns across different tasks. However, substantial dissimilarities emerged in the evaluation criteria teachers used to assess students' performance. A qualitative analysis demonstrated that the judgments of the NS teachers were more detailed and elaborate than those of the NNS teachers in the areas of pronunciation, specific grammar use, and the accuracy of transferred information. These findings are used as the basis for a discussion of NS versus NNS teachers as language assessors on the one hand and the usefulness of mixed methods inquiries on the other.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".