The language assessment literacy needs of Iranian EFL teachers with a focus on reformed assessment policies
Bibliographic record
Abstract
Teachers’ assessment literacy has recently captured the attention of scholars across various educational contexts. The literature has it that there is a gap between teachers’ assessment practices and national assessment policies. The present study investigated the assessment needs of Iranian EFL teachers in the wake of the new assessment reform, which aims at replacing traditional discrete point testing policies with performance testing. In-depth interviews were conducted with 15 EFL head teachers. In addition, documents related to the curriculum reform were also closely examined. Inductive coding of the data showed that to meet the demands of the noted reform, teachers’ current perceptions of language assessment need to change. Furthermore, teachers need training in both knowledge and skills of language assessment. More specifically, teachers need training in developing rubrics for use in assessing the productive skills of speaking and writing. They also need to develop literacy in devising higher-order thinking skills in assessing reading and listening comprehension. Finally, as non-native speakers of English, Iranian English teachers need better English aural/oral skills.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".