Context Clue Strategies for Business English Vocabulary and Engagement Among EFL Undergraduates
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.
Bibliographic record
Abstract
The purpose of this study was to: (1) assess the effectiveness of context clue strategies in learning Business English vocabulary, (2) compare students' vocabulary comprehension before and after instruction, and (3) examine students' perceptions and engagement. This quasi-experimental study used a one-group pretest-posttest design with 30 third-semester business administration students selected through purposive sampling. The intervention was conducted over six weeks (three hours per week), covering Business Result Pre-intermediate (Units 1–6). Data were analyzed using descriptive statistics, paired t-tests, and effect size calculations. Statistical results showed: (1) Strategy performance (E₁/E₂ = 85.67/82.23) surpassed the criterion of 80/80; (2) Post-instruction scores (M = 24.67, SD = 2.85) were significantly higher than pre-instruction scores (M = 18.73, SD = 3.42), t = -8.94, p < .001, with a very large effect size (Cohen's d = 1.94); and (3) Students expressed strongly agreed regarding the strategy's effectiveness (M = 4.24, SD = 0.65) and demonstrated high levels of engagement (M = 4.18, SD = 0.63). The findings demonstrate that context clue strategies significantly contribute to business English vocabulary learning and enhance student engagement.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it