What needed to be understood.
The project needed to move beyond the familiar claim that gaming automatically harms grades. It also required careful interpretation of coded categories, group sizes, and observational relationships.
How the analysis was built.
- Cleaned and relabeled coded variables in Excel.
- Built three Tableau dashboards covering gaming, parental education, and family income.
- Used correlation analysis, group averages, filters, scatterplots, and bar charts.
- Developed a storyboard and presented the findings through a recorded data story.
What the evidence showed.
- Gaming hours showed a weak negative relationship with grades, while gaming frequency showed almost no linear relationship.
- Mother’s education had the strongest positive association in the selected variables, followed by father’s education.
- Female students averaged higher grades than male students in this dataset.
- Gamers represented most perfect scores because they represented most of the sample; non-gamers had the higher perfect-score rate.
What should happen next.
- Focus on balanced screen-time guidance rather than blanket gaming bans.
- Use family context only to offer voluntary support—not to lower expectations or deterministically profile students.
- Collect sleep, wellbeing, study time, domestic context, and game-type variables in future surveys.
What this project does—and does not—prove.
This was an observational academic dataset. Associations do not establish causation, and the meaning of the zero-income category requires verification before drawing socioeconomic conclusions.