Comparison of English Vocabulary Mastery Between Computer-Gamer and Non-Gamer Indonesian Students
Bibliographic record
Abstract
Game has been a part of teenagers’ lives. The advancement of technology has led to the development of computer games. The vocabularies from games could give ample exposure to those who play them. The present study reports the difference in English vocabulary mastery of the computer-gamer and non-gamer Indonesian students and the correlation between frequency of playing computer games and the English vocabulary mastery. The research designs employed were comparative and correlational studies. The participants, 72 eleventh grade students of SMK Negeri 1 Bangil Pasuruan majoring Multimedia Engineering, were divided into two groups, 36 computer-gamer students and 36 non-gamer students. The data were collected by utilizing a demographic data collection and a free completion test of English vocabularies. The collected data were then analyzed statistically using SPSS 20. The results revealed that there was no statistically difference in English vocabulary mastery between computer-gamer students and non-gamers for the -value was 0.589. The result of Pearson correlation which was used to answer the second research question showed that there was a positive but very weak correlation between frequency of playing computer games and the English vocabulary mastery. It could be inferred from the result that playing games does not really support the vocabulary acquisition of the students and the amount of time spent to play games barely improve their vocabulary mastery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.003 | 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".