The Effects of Test Type, Pronunciation, and Proficiency Level on EFL Learners’ Speaking Exam Scores
Bibliographic record
Abstract
The present study aims to reveal the effects of test type, pronunciation and proficiency levels of the students on speaking test scores. A total of 147 Turkish EFL students consisting of 38 beginner, 36 elementary, 37 pre-intermediate and 36 intermediate levels participated in the study. Presentation as planned, and paired speaking test as unplanned one were used as instruments to figure out the effects of two different test types on the test scores. Being prepared for their performances, the students did their presentations lasting between 5 and 8 minutes in front of two raters. The same participants, at the end of each level, were invited to the paired-speaking tests designed for the students to show their spontaneous performances. The performances in both tests were scored through a scale with five criteria, and the criteria along with overall scores were examined through Paired Samples t-test to reveal the effects of test type, and through bivariate regression to see the proportion of pronunciation aspect on overall scores. The results showed that in beginner level, although no differences were found in the overall scores, the test type induced differences in pronunciation, vocabulary and relevance aspect of their speaking performances. Similar results were found in elementary level besides the difference found in accuracy aspect, which resulted in a significant difference in overall scores. However, in pre-intermediate level, the only significant difference was found in pronunciation. On the other hand, in intermediate level all the aspects along with the overall scores were found to be affected by the test type except for fluency and pronunciation. The bivariate regression revealed that the effect of pronunciation sub-score on overall scores is significant in each level and test type.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".