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Implementation of the CEFR in the rubrics of two main English Certificates: Cambridge FCE and Trinity ISE-II.

2021· article· en· W3182795032 on OpenAlexaff
Lucía Fraga Viñas

Bibliographic record

Venue˜El œGuiniguada · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsTrinity College
Fundersnot available
KeywordsRubricSyllabusSpanish languageHumanitiesCurriculumEnglish languagePedagogyLibrary sciencePolitical scienceSociologyArtLinguisticsPhilosophyComputer science

Abstract

fetched live from OpenAlex

The Common European Framework of Reference (CEFR) was developed by the European Council with the intention of providing a comprehensive basis for the creation language syllabi and curriculum guidelines, together with the design of teaching materials, language certificates and instruments of assessment. The CEFR has been implemented in Spain through different education laws and has prompted the introduction of the communicative approach and the use of new instruments of assessment such as rubrics. Nonetheless, almost twenty years after the CEFR was passed, not many researches have been conducted on how the Framework has been implemented. It is from this line where the current research stems as it intends to check how the CEFR has been adapted in the rubrics used for the assessment of the writing skill in two main English Certificates: the Cambridge Assessment English FCE and the Trinity College ISE-II. El Marco Común Europeo de Referencia (MCER) se desarrolló con el objetivo de promover una base común para la creación de currículos educativos y servir como guía en la elaboración de materiales didácticos, exámenes de certificación e instrumentos de evaluación. El MCER se ha implantado en España a través de diferentes leyes educativas y ha propiciado la introducción del enfoque comunicativo o nuevos instrumentos de evaluación como rúbricas. Sin embargo, casi veinte años después de la aprobación del marco, pocos son los estudios que han revisado de qué manera se ha adaptado el marco. Es aquí donde la presente investigación se sitúa, con el objetivo de comprobar cómo se ha implementado el MCER en las rúbricas para examinar la destreza escrita de dos de los principales certificados de inglés: el Cambridge Assessment English FCE y el Trinity College ISE-II.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.279
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 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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