Gaming Academic Performance

By shrijeetverma13 · May 8, 2026

This dataset explores the relationship between gaming habits and academic performance among students. It includes key lifestyle factors such as study…

The analysis reveals that gaming genre has very little impact on academic performance. Casual gaming is associated with the best academic grades (average 66.44), while RPG gaming is linked to the worst performance (average 65.96). However, the gap between the best and worst genres is only 0.48 points — an extremely small difference suggesting genre choice barely influences grades. Two visualizations were generated: a bar chart showing average grades by genre and a box plot displaying the full grade distribution across genres.

The analysis produced two visualizations and data tables comparing grades, attendance, and gaming hours across Low, Medium, and High stress levels. A grouped bar chart shows the average values for each metric side by side across stress groups, making it easy to spot trends. A box plot reveals the full distribution and spread of each metric per stress level, with gaming hours scaled by 10x for easier comparison on the same axis.

The winning combination is studying 8.2–10 hours per day paired with sleeping 8–9 hours per night, which produces an average grade of 95.04 based on 308 students. This suggests that both maximizing study time and getting a full night's sleep are key to top academic performance. Two visualizations were generated: a heatmap showing average grades across all study/sleep combinations, and a bar chart highlighting the top 5 best-performing combinations.

The analysis reveals how addiction score relates to three key academic and wellbeing variables. Three interactive scatter plots and a bar chart were generated to visualize these relationships. Addiction score has a moderate negative correlation with grades (r = -0.495), meaning higher addiction scores tend to be associated with lower grades. Interestingly, addiction score shows virtually no correlation with attendance (r = 0.000), suggesting attendance is not meaningfully linked to addiction levels. There is also a moderate negative correlation with stress level (r = -0.501), indicating that students with higher addiction scores tend to report lower stress levels — which may reflect a complex or counterintuitive relationship worth exploring further.

The analysis generated data tables comparing behavioral profiles between top-performing and low-performing students. Three tables were produced showing key behavioral metrics and stress level distributions across performance groups, giving a clear picture of how these two groups differ in their habits and characteristics.

Gaming hours have a strong negative impact on academic grades. Students who game only 0–2 hours average a grade of 81.72, while those gaming 6–8 hours drop to an average of 48.59 — a 40.5% decline. The overall correlation between gaming hours and grades is -0.551, indicating a meaningful negative relationship. Three interactive charts and multiple data tables were generated to explore this across gender, stress level, and gaming genre segments.

The Pearson correlation analysis has been completed across all 9 behavioral and academic factors. Two visualizations were generated: a full correlation heatmap with annotated coefficients, and a bar chart showing how each factor correlates with grades. The strongest positive correlation in the dataset is between gaming hours and addiction score (r = 0.91), meaning students who game more tend to have significantly higher addiction scores. The strongest negative correlation is between gaming hours and reaction time ms (r = -0.94), suggesting heavier gamers actually have faster reaction times (lower ms values).

The analysis successfully identified outliers in both the addiction score and device usage columns using the IQR method, and compared academic metrics between outlier and non-outlier student groups. Two visualizations were generated: a grouped bar chart comparing average grades, sleep hours, and attendance between outliers and non-outliers, and boxplots showing the distribution of addiction scores and device usage with their outlier boundaries. Additionally, detailed data tables were produced summarizing the IQR statistics and group comparisons.

The analysis compared Male and Female students across 6 key performance metrics and stress level distributions. Two grouped bar charts were generated — one showing mean performance metrics by gender, and another showing stress level distributions (%) by gender. The results reveal remarkably similar patterns between genders across all measured dimensions.

The analysis produced two visualizations exploring reaction time patterns across students. The histogram with KDE curve shows that reaction times are roughly symmetric and normally distributed, centered around a mean of 271.11 ms and a median of 270.48 ms — nearly identical, confirming the symmetric shape. The standard deviation is 29.44 ms, meaning about 68% of students fall between 241.67 ms and 300.55 ms. The box plot breaks down reaction times by gaming genre, showing the spread and median for each genre side by side, making it easy to compare whether genres like FPS or Casual are associated with faster or slower responses.