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Record W2923578044 · doi:10.1186/s40468-019-0078-7

The language assessment literacy needs of Iranian EFL teachers with a focus on reformed assessment policies

2019· article· en· W2923578044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage Testing in Asia · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsQueen's University
Fundersnot available
KeywordsRubricPsychologyLiteracyCurriculumPedagogyActive listeningMathematics educationLanguage assessmentAlternative assessmentPerceptionFocus groupSociology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.360
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it