Unpacking Vietnamese EFL Learners’ Deployment of Reading Test-Taking Strategies for the New TOEIC Test Format
Bibliographic record
Abstract
Test-taking strategy (TTS) plays a vital role in accomplishing the tests, and many types of tests require test-takers to use TTSs differently. TOEIC (Test of English for International Communication), one of the standardized tests, features two main parts, viz. listening and reading, requesting test-takers to deploy their TTSs intensively to accomplish the test effectively. Reading TTSs are of importance for test-takers in responding to the reading content. Nevertheless, test-takers in different learning ecologies utilize reading TTSs dissimilarly. Therefore, this study was to examine reading TTSs for the new TOEIC new format utilized by EFL learners at a language center in Ho Chi Minh City, Vietnam. This mixed-methods study employed two research instruments, namely questionnaire and semi-structured interview, for data collection. A cohort of 221 EFL learners was recruited to answer the questionnaires, and 25 of them were invited for semi-structured interviews. The SPSS software (version 26.0) was used to process the quantitative data gathered from the questionnaires, while the content analysis approach was utilized to analyse the qualitative data collected from the semi-structured interviews. The results revealed that research participants deployed the reading TTS for the new TOEIC test format at a high frequency. Additionally, participants were found to deploy the memory and compensatory strategies for TOEIC reading tests more frequently than cognitive, metacognitive, and affective ones. From the obtained findings, pedagogical implications are suggested to leverage the quality of reading teaching and learning in general and TOEIC training in specific.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".