Comparison of Native and Non-native English Language Teachers’ Evaluation of EFL Learners’ Speaking Skills: Conflicting or Identical Rating Behaviour?
Bibliographic record
Abstract
Assessing speaking skills is regarded as a complex and hard process compared with the other language skills. Considering the idiosyncratic characteristics of EFL learners, oral proficiency assessment issue becomes even more important. Keeping this situation in mind, judgements and reliability of raters need to be consistent with each other. This study aims to compare native and non-native English language teachers’ evaluation of EFL learners’ speaking skills. Based on the oral proficiency scores in the final exam conducted at a state university in Turkey, the study analysed the scores given by native and non-native English language teachers to 80 EFL students attending preparatory classes in the 2014-2015 academic year. 3 native and 3 non-native English language teachers participated in the study. Data were collected through an analytic rating scale and analysed with the help of independent samples t-test and Pearson product-moment correlation test. Pearson product-moment correlation test (calculated as 0,763) indicated that the raters had high inter-rater reliability coefficients. T-test results revealed that there is no statistically significant difference in the total scores given by both groups of teachers. The study also investigated the different components of speaking skills such as fluency, pronunciation, accuracy, vocabulary, and communication strategies with regard to the existence of significant difference between the scores. The only component which created a statistically significant difference was found to be pronunciation, which was expected prior to the research. In line with the overall findings of the study, it can be concluded that native and non-native English language teachers display almost identical rating behaviour in assessing EFL students’ oral proficiency.
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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 | 0.011 | 0.038 |
| 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 | 0.000 | 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".