Implementing Minecraft as a Tool to Teach Vocabulary in a Saudi Intermediate School: An Experimental Study
Bibliographic record
Abstract
Utilizing digital games for learning vocabulary can be auspicious. Thus, the current study investigates the potential effectiveness of implementing Minecraft to teach vocabulary. The participants who underwent this study were eighteen students of intermediate Saudi schools in Riyadh. They are 12-13 years old. They were grouped into two groups: the experimental and control groups. The pupils were randomly chosen. Both groups were taught the same vocabulary. While the control group was taught via the traditional methods, the experimental group was taught by using Minecraft. The experiment lasted two weeks before the post-test was carried out. The researcher made an observation sheet to examine students' acceptance of employing Minecraft and their behaviors towards it. The results of the pre-post tests were analyzed through SPSS. The present study's findings revealed a significant distinction in favor of the experimental group, which was taught using Minecraft. In addition, the students exhibited a positive attitude towards Minecraft. Besides, it is desired that teachers support other learning methods, including games that raise creativity and construct a comfortable condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".