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Record W4323529547 · doi:10.20448/gjelt.v2i1.4128

Investigating the Validity of the IELTS Listening Test

2022· article· en· W4323529547 on OpenAlexaff
Peter Peltekov

Bibliographic record

VenueGlobal Journal of English Language Teaching · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsActive listeningTest (biology)Construct validityPopularityPsychologyConstruct (python library)Reading (process)Test validityLanguage assessmentMathematics educationComputer scienceLinguisticsPsychometricsClinical psychologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

The International English Language Testing System (IELTS) has gained popularity in recent years and it is now accepted by many educational institutions worldwide. While IELTS offers a distinct academic version of the reading and writing test components, it uses the same listening module for the General Training and the Academic exam. The present article explores to what extent the listening subtest in the Academic IELTS test is a useful measure of test-takers listening ability and a predictor of their academic success. This study focusses on the test’s construct validity. A critical review of the major strengths and potential drawbacks of the listening test is followed by a comparison of the test scores with another traditionally accepted test of academic English. Conclusions about the validity of the IELTS listening test are drawn along with some suggestions for design improvement. Future research directions are also proposed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.131
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.131
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.288
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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