Linear Regression Algorithm the Effect of Game Time on Students' Reading Interest
Bibliographic record
Abstract
Education plays an important role in shaping an individual's character and abilities, with students' interest in reading as the main foundation for intellectual development and critical thinking skills. However, in today's digital age, there has been an alarming decline in students' interest in reading, who are more interested in spending their free time playing games than reading. The development of information technology and digital entertainment such as smartphones, tablets, and computers has changed students' habits in utilizing their free time. Playing games is the main choice, while reading interest is marginalized, which has a negative impact on students' literacy skills. The decline in reading interest has serious implications, especially in Indonesia, where the Human Development Index (HDI) in the field of education is still low compared to neighboring Malaysia. This low interest in reading is influenced by the lack of reading habits from an early age and unequal access to education. Good reading skills not only affect academic achievement but also the development of critical, analytical, and creative thinking skills. This study aims to understand the factors that affect students' interest in reading, especially the influence of game time. The Simple Linear Regression method was used to analyze the relationship between game play time and students' reading interest, which provided valuable insights for educators and parents in designing effective educational strategies. The study focused on SMP Negeri 7 Binjai and used a linear regression method to analyze data on students' reading and gaming habits. The results of the study show that excessive game playing time, an average of 9 to 10 hours per day, has an impact on increasing and decreasing students' interest in reading. This data was analyzed using the RapidMiner application, which showed a correlation between playing games and students' reading interest.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".