Visual Timelines-based Technique for Enhancing Saudi EFL Learners’ Understanding of Tenses in English
Bibliographic record
Abstract
Drawing on a widely known adage - ‘a picture is worth a thousand words’-, the current study attempts to explore the potential of a visual timelines-based technique for enhancing Saudi EFL learners’ understanding of tenses in English. A quasi-experimental design was employed, involving forty-two male Saudi undergraduate EFL students, with 20 in the experimental group and 22 in the control group. Both groups underwent pre- and post-tests to assess their performance, while the experimental group also completed a questionnaire. The study findings indicate that the use of visual timelines had a positive influence on the participants’ understanding of English grammar tenses. The technique was found to be effective in providing clear information and directions, demonstrating the connections between tense forms and time references, saving learners’ time and effort, and boosting their motivation. The study concludes with recommendations based on the findings and suggests further research addressing the study’s limitations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".