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Record W4320512496 · doi:10.2991/978-2-494069-89-3_252

A Comparative Analysis of CET, IELTS and TOFEL for English Acquisition

2022· book-chapter· en· W4320512496 on OpenAlexaff
Xiaoyu Zhang

Bibliographic record

VenueAdvances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2022
Typebook-chapter
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceNatural language processingLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

English is the most widely distributed language in the world.With more and more international cultural exchanges, there are a huge number of people who learn English as a second language.English has always been the most popular second language in China.International language tests such as IELTS and TOEFL are becoming more common in China as more Chinese students go abroad for undergraduate or graduate studies.Compared with the two international English proficiency tests, CET-4 and CET-6 are basic English proficiency tests in China.This type of test primarily examines the student's basic vocabulary quantity to comprehend the situation and the grammar knowledge of the actual application.While IELTS focuses more on the assessment of logical thinking and language expression skills, reading and speaking tests are the most difficult assessment tasks.TOEFL attaches more importance to the students' listening, speaking, reading, and writing skills combined with the use of English level, and the difficulty of each part is evenly distributed, strong academically.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0200.002

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 head0.112
GPT teacher head0.487
Teacher spread0.375 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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