Teaching English Vocabulary for Specific Purposes: A Case Study of Arts Vocabulary
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".