Compulsive Internet Use and Academic Performance among Senior High School Students in Lipa City
Bibliographic record
Abstract
In today's digital age, the internet has become integral in the lives of individuals. However, while it offers numerous benefits, it is undeniable that compulsive internet utilization may have potential challenges, especially for students and their academic performance. This research identified and measured the levels of compulsive internet use and academic performance among senior high school students of Lipa City. The researchers used a quantitative descriptive correlational design to describe research variables and their relationship. A purposive sample method was used to select the 55 students from one institution in Lipa City. The Compulsive Internet Use Scale was used to measure the compulsive internet use of students while the academic performance was based on the grade point average obtained from the senior high school department. The present study found that the respondents have moderate compulsive internet use and satisfactory academic performance. A negative relationship was also found between the variables (r=-.295), indicating that higher levels of compulsive internet use among respondents corresponded to lower academic performance. Likewise, statistical analysis indicated that the relationship between the two variables was significant (p=.029). Therefore, the present study recommends that the parents, teachers, and the institution's administration should collaborate to formulate and spearhead an intervention program to reduce compulsive internet use.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".