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 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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".