Multiple Choice Test-Taking Strategies, Test Anxiety, and EFL Students’ Achievement
Bibliographic record
Abstract
Successfully taking a multiple-choice test requires understanding the testing situation and knowing how to take the test efficiently. This study analyses the relationship between multiple-choice test-taking strategies (MCTTSs), test anxiety and the English language achievement of English as a Foreign Language (EFL) students. A mixed-methods approach – quantitative and qualitative – is used by collecting data using a questionnaire and interviews, to increase the research validity. The MCTTS questionnaire of Nguyen (2003), and the test anxiety scale of Aydin et al. (2006) and Burgucu et al. (2011), were used. A total of 727 male and female students from different academic levels and tertiary colleges at an English language centre were chosen as samples. The results broadly show a positive correlation between MCTTSs and English language achievement and a negative relationship between MCTTSs and test anxiety. However, the results revealed significantly higher English language achievement by the female students than the male students. However, there was no difference in the MCTTSs and the degree of test anxiety according to gender. The results suggest raising teachers’ and students’ awareness of the importance of using test-taking strategies. Furthermore, the results can help English language instructors to explain test scores from a different viewpoint, to provide a reliable assessment of language students’ true competence and to reduce the likelihood of measurement errors.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".