A Study on Digital Games Internet Addiction, Peer Relationships and Learning Attitude of Senior Grade of Children in Elementary School of Chiayi County
Bibliographic record
Abstract
The study explored the relationships between digital games internet addiction, peer relationships, and learning attitudes through questionnaire survey in senior grade of elementary school children in elementary school in Chiayi County. Taking 735 students as the research object, SPSS 22.0 as a quantity research analysis instrument was applied to launch descriptive statistics, t-test, and single factor variation analysis, Pearson Product Difference Correlation Analysis, and Stepwise Multiple Regression Analysis. The research results found that 93.2% of high-grade students in Chiayi County played digital games 2 to 3 times a week, and those who took less 1 hour to play digital games. The addiction level of digital internet games for schoolchildren, peer relationships and learning attitude ranked upper-middle level. There are obvious differences in digital gaming internet addiction, peer relationships, and learning attitudes among various background variables such as “school location”, “weekly use” and “daily use time”. Gaming internet addiction has a low degree of negative correlation in both peer relationships and learning attitudes, while the peer relationships has a moderately positive relation with learning attitudes. Peer-to-peer relationships may be highly predictive of learning attitudes. This research focuses on the educational problems of digital game internet addiction that primary school teachers are most likely to face. It explores the relationship between peer relationships and learning attitudes, fills in the current lack of research, and draws the above important findings. It is suggested that it can be used as an important reference for teachers, parents and follow-up researchers, and contribute to the academic research of primary education in Taiwan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".