The Study of Internet Use and Academic Achievement of Elementary Students in Bangkok
Bibliographic record
Abstract
The purpose of this study is to investigate the relationship between internet usage behavior and academic achievement among elementary school students from grade 4-6 in Bangkok. The researcher employed Multi-stage Sampling to recruit 297 samples. The data was gathered via the following tests: 1) Intelligence tests, namely Colored Progressive Matrices (CPM) for students aged 5-11 year old or Standard Progressive Matrices (SPM) for 12 year old and above, and 2) Academic achievement test, namely Wide Range Achievement Test Thai Edition: WRAT-Thai. The findings revealed that time spent on the internet is negatively correlated to student’s reading achievement (r = -.24, p < .001), spelling achievement (r = -.26, p < .001), and math achievement (r = -.20, p = .001). More surprisingly, academic related internet usage was also found to be negatively correlated to math achievement (r = -.20, p < 0.05). Meanwhile, internet usage for social media has a correlation with academic achievement in math and reading, (r = -.20, p = .001) and (r = -.13, p < .05), respectively. Moreover, internet usage for entertainment was found to have a negative correlation with academic achievement in reading, spelling and math, (r = -.25, p < .001), (r = -.27, p < .001) and (r = -.21, p < .001), respectively. Internet usage for online business, however, yielded no correlation to academic achievement. The study concluded that daily internet usage does have an effect on academic achievement in math. Moreover, when used for entertainment and social media, internet usage can pose a negative effect on academic achievement in reading and writing.
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.
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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".