A Quantitative Study in Using Digital Games to Enhance the Vocabulary Level of Saudi Male Secondary School Students
Bibliographic record
Abstract
Vocabulary learning is crucial to language acquisition. Although numerous techniques have been proposed for the teaching and learning of vocabulary, the need remains for the research and development of new, effective methods. In this technological era, digital games have proven their efficacy in promoting learners’ vocabulary acquisition. The current study investigates whether the integration of technology-driven digital games is effective in enhancing the vocabulary level by comparing experimental and control groups. The researcher conducted a pretest for the experimental group and a posttest for both groups within a period of five consecutive weeks. The experimental group used 7 Little Words, which is a game for learning vocabulary, whereas the participants in the control group learned vocabulary through traditional methods. The sample comprised 30 Arabic native speakers studying English as a required course in two all-male classes (15 students from each) in the third year of secondary school. Data were analyzed quantitatively. Paired-samples t-tests and independent-samples t-tests were used to compare the mean scores of the two groups. The results indicated that using digital games to learn vocabulary enhanced learners’ overall vocabulary acquisition.
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How this classification was reachedexpand
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".