MétaCan
Menu
Back to cohort
Record W4282931634 · doi:10.5539/elt.v15n7p75

Validation of the Results of Linking Speaking Test of IELTS to China’s Standards of English Language Ability

2022· article· en· W4282931634 on OpenAlexvenueno aff
Yuyue Chen, Xue-Feng Wu

Bibliographic record

VenueEnglish Language Teaching · 2022
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralizability theoryPsychologyConsistency (knowledge bases)Test (biology)Mathematics educationDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

This study, taking into consideration of teachers’ as well as students’ judgments, explored the generalizability and consistency of the results of linking speaking test of IELTS to China’s Standards of English Language Ability (CSE). Nine college English teachers and 81 undergraduate students judged the degree of congruence between the IELTS speaking test and 72 relevant CSE descriptors to generate evidence of generalizability; 2 teachers judged the CSE levels of 11 videos of IELTS speaking test and 113 students assessed themselves based on the self-assessment scale, to provide data for exploring consistency from three perspectives: consistency of teachers’ judgments, consistency between teachers’ judgments and empirical scores, and consistency between students’ actual ability and the ability demonstrated in the self-assessment scale. The evidence showed that the linking results performed well both in generalizability and consistency from the perspective of both teachers and students, but students rated comparatively low than teachers both in the two facets. However, considering that certain descriptors are relatively abstract and separate from daily life situations in IELTS speaking test, it is understandable that students rate low in generalizability; and considering students' insufficient self-assessment ability, their relative low recognition in consistency also get explained. In general, therefore, the linking results show good generalizability and consistency. 

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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
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 science0.0010.001
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 head0.008
GPT teacher head0.286
Teacher spread0.279 · 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 designQualitative
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

Explore more

Same venueEnglish Language TeachingSame topicEducational Technology and AssessmentFrench-language works237,207