The Development of STEP, the CEFR-Based English Proficiency Test
Bibliographic record
Abstract
This study aimed at developing the Silpakorn Test of English Proficiency (STEP), in alignment with the Common European Framework of Reference for Languages (CEFR), and in accordance with the theoretical framework established by Alderson et al. (2006). Four major steps were involved in the test construction. First, English language lecturers who served as content specialists were asked to design can-do statements presented in the CEFR. Then the specialists designed the test specification based on the can-do statements. Four skill areas: listening, semi-speaking, reading, and semi-writing were targeted as the test construct. At this juncture, the content specialists were required to write test items in accordance with the test specification. Next, the test items constructed were determined for their validity and reliability. Finally, a standard setting was carried out. The results demonstrated that the framework offered by Alderson et al. (2006) served as an effective reference document for developing the STEP. In terms of validity and reliability, the STEP was of statistical significance, that is, it could be aligned with the CEFR levels and measure test takers’ English proficiency at a specific CEFR level. The current findings provide useful insights for test developers or researchers who wish to design proficiency tests in alignment with the CEFR.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".