Comparison of English Vocabulary Mastery Between Computer-Gamer and Non-Gamer Indonesian Students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it