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THE USE OF GRAMMAR TRANSLATION METHOD IN ENGLISH LEARNING TO THE SUB-DISTRICTS’ JUNIOR HIGH SCHOOLS IN TABANAN REGENCY

2023· article· en· W4381618557 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSuluh Pendidikan · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsGrammarDocumentationVocabularyComputer scienceMathematics educationReading (process)Data collectionQualitative propertyProcess (computing)LinguisticsPsychologySociologyProgramming language

Abstract

fetched live from OpenAlex

Grammar Translation Method is regarded as the old method that still used particularly at the school in sub-districts area. This research purposed to describe the use of Grammar Translation Method in English learning to the sub-districts junior high schools in Tabanan regency. The subjects of this research were three English teachers and 124 students. The data collection techniques used in this research were observation, interview, and documentation. Qualitative data analysis was applied to analyze the data. In accordance with data analysis, it was obtained that (1) Grammar Translation Method (GTM) was used in learning process includes the phases of observing, questioning, collecting data, associating, communicating, (2) Grammar Translation Method was used to increase students’ knowledge and skills in reading and writing. (3) The difficulties faced by the students were utilizing the appropriate vocabulary and structuring sentences. The conclusion of this research is the use of Grammar Translation Method is still required to increase students’ capability and skills, especially in reading and writing.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.074
GPT teacher head0.332
Teacher spread0.258 · 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