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Record W4416407102 · doi:10.5539/jel.v15n2p66

Teaching English Vocabulary for Specific Purposes: A Case Study of Arts Vocabulary

2025· article· W4416407102 on OpenAlexvenueno aff
Jintana Chatburapanun, Tipa Thep-Ackrapong

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Language
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyThe artsEnglish languageVocabulary learningTeaching methodEnglish vocabularyLanguage arts

Abstract

fetched live from OpenAlex

This research aimed at three objectives: first, to compare the effectiveness of direct and indirect teaching vocabulary; second, to examine the appropriateness of each instructional approach across varying levels of English language proficiency; and finally, to study the students’ satisfaction using these two methods. The students involved were Fine Arts majors studying English for Specific Purposes (ESP). Non-equivalent groups of 156 students enrolled in English for Careers in the 21st Century in an art academy in Bangkok, Thailand were recruited in the study. The research was a quasi-experimental design within a pretest-posttest framework. Several research instruments were utilized: 1) Fine Arts and Applied Arts Vocabulary Lists, 2) a 30-item 4-choice pretest and posttest, and finally, 3) a satisfaction survey. The results revealed no statistically significant difference between the direct and indirect instruction groups. However, direct vocabulary instruction was more effective than indirect instruction, particularly for learners with limited English proficiency. Furthermore, students from both groups reported high satisfaction levels with various aspects of the course, including the teaching methods, materials, activities, learning environment, and the vocabulary lists for fine arts and applied arts. These results support the potential of direct vocabulary teaching methods in enhancing student performance while fostering overall satisfaction in specialized language learning contexts.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.364
Teacher spread0.342 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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