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Record W2797137296 · doi:10.5539/elt.v11n5p55

English in Public Schools Located in Metropolitan Lima, Peru: An Analysis of Eleventh-Grade Students’ Level and Perceptions

2018· article· en· W2797137296 on OpenAlexvenueno aff
Andrea I. Morales, Paola S. Palomeque, Valeria Paredes, Jérôme Mangelinckx

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsEleventhMetropolitan areaContext (archaeology)Mathematics educationPsychologyPerceptionTest (biology)Sample (material)English languagePlan (archaeology)Lesson planPedagogyMedical educationGeographyMedicine

Abstract

fetched live from OpenAlex

English Language Teaching (ELT) public policies are present in most of the countries of the Americas due to the importance of said language in the international context. The objective of this research was to know the English level of eleventh-grade students in public schools located in Metropolitan Lima, Peru, as well as their perceptions of their own English learning process within the framework of the new national plan called Inglés, Puertas al Mundo (English, Doors to the World). The sample was composed of 72 students from four schools of the city. This study was conducted using a mixed-method (quantitative-qualitative) approach. The instruments used were a standardized English test (New Inside Out Quick Placement Test) and a structured interview guide. The results revealed that the students’ English level is below the level outlined in the national policy. Regarding the perception of their own learning, students have different motivations to learn English, and enjoy the blended model introduced by the national English plan. However, they agree that their English level is very basic and that they would not be able to reach the established communication objectives after graduating from high school.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.298
Teacher spread0.266 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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