MétaCan
Menu
Back to cohort
Record W2038816208 · doi:10.5539/hes.v1n2p107

Fairness of IELTS Test Scores in University Admission

2011· article· en· W2038816208 on OpenAlexvenueno aff
Motahar Khodashenas Tavakkoly

Bibliographic record

VenueHigher Education Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Context (archaeology)Language proficiencyPsychologyLanguage assessmentInterpretation (philosophy)Empirical researchMedical educationMathematics educationApplied psychologyMedicineComputer scienceStatistics

Abstract

fetched live from OpenAlex

In recent years there has been growing theoretical interest in exploring the relationship between the interpretationand use of high-stakes proficiency test scores. In these discussions, the role of institutional test users (or test scoreconsumers) has received only limited attention. This may be due, at least in part, to the lack of consensus in theliterature about the degree of responsibility test users have for the valid and ethical interpretation and use of testscores. To date, there has also been very little empirical research on the work of these stakeholders. This articlereports on a study focusing on how the International English Language Testing System was used in the selection ofstudents in an EFL context at an Iranian university and the knowledge and beliefs that test users (administrative andacademic staff) had about the test. The central issues raised for readers by this paper is the possibility thatjudgements made on the basis of IELTS may not correlate with the subsequent performance of students, and thatflaws or strengths in these performances may be correlated with IELTS scores and the ensuing entry judgements.The results suggested that there were a number of serious flaws in the interpretation and use of test scores at thisinstitution. Recommendations are made for improving the use of English proficiency evidence and the assessmentliteracy of staff in universities in Iran or other places around the world.

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 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)0.000
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 score0.094
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.116
GPT teacher head0.396
Teacher spread0.280 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueHigher Education StudiesSame topicStudent Assessment and FeedbackFrench-language works237,207