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Record W2992731386

Iranian Candidates' Attitudes toward TOEFL iBT

2015· article· en· W2992731386 on OpenAlexaboutno aff
Samineh Poorsoti, Hanieh Davatgari Asl

Bibliographic record

VenueJournal of applied linguistics and language research · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsTest of English as a Foreign LanguageTest (biology)Graduation (instrument)PopularityPsychologyThe InternetScholarshipMedical educationMathematics educationEnglish languageSocial psychologyPolitical scienceComputer scienceMedicineMathematicsLaw
DOInot available

Abstract

fetched live from OpenAlex

Test of English as a Foreign Language (EFL) stands as an important criterion for admission, scholarship and graduation decisions to colleges and universities in the United States, England, Australia, New Zealand, Canada and some other countries. Since 2005, the Internet based form of the TOEFL test has gained popularity across the world, and test takers from various countries take this test online. This study aimed at investigating the attitudes of Iranian candidates towards the Internet-based TOEFL. For this reason, an attitude questionnaire was distributed among 56 Iranian examinees who had taken this test in Iran. The questionnaire was validated in a pilot study to ensure its reliability and validity. Almost 22 days after taking the test, the candidates' test scores were announced and collected through their e-mails by the researcher. To find out the relationship between the participants' test scores and their attitude toward different sections of the test, the Pearson correlation coefficient was conducted. The obtained results showed that there was only a relationship between the candidates' overall attitude toward the test and their test scores.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.139
GPT teacher head0.386
Teacher spread0.247 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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