The Impact of Gaming Hours on Learning Performance

Author

Your Name

Published

June 25, 2026

1 Introduction

Video games are often viewed as entertainment, but some researchers suggest that gaming may improve cognitive abilities such as attention, memory, and problem-solving skills.

This study investigates whether the amount of time spent playing video games is associated with learning performance.

1.1 Research Question

Does the number of gaming hours per week affect learning performance?

1.2 Hypothesis

1.2.1 Null Hypothesis (H0)

Gaming hours have no significant effect on learning performance.

1.2.2 Alternative Hypothesis (H1)

Gaming hours positively affect learning performance.

2 Load Required Libraries

library(tidyverse)

3 Data Import

In a real research project, data would be collected from students through surveys and learning assessments.

For demonstration purposes, we create a sample dataset and save it as a CSV file.

gaming_learning_demo <- data.frame(
  student_id = 1:12,
  gaming_hours = c(2,4,5,7,8,10,12,14,15,18,20,22),
  learning_score = c(60,64,66,70,73,77,80,84,86,89,91,94)
)

write.csv(
  gaming_learning_demo,
  "gaming_learning.csv",
  row.names = FALSE
)

Import the dataset.

gaming_learning <- read.csv("gaming_learning.csv")

head(gaming_learning)

  student_id gaming_hours learning_score
1          1            2             60
2          2            4             64
3          3            5             66
4          4            7             70
5          5            8             73
6          6           10             77

4 Data Cleaning (Tidying)

Inspect the structure of the dataset.

str(gaming_learning)

'data.frame':   12 obs. of  3 variables:
 $ student_id    : int  1 2 3 4 5 6 7 8 9 10 ...
 $ gaming_hours  : int  2 4 5 7 8 10 12 14 15 18 ...
 $ learning_score: int  60 64 66 70 73 77 80 84 86 89 ...

Check for missing values.

colSums(is.na(gaming_learning))

    student_id   gaming_hours learning_score 
             0              0              0

Remove missing values if necessary.

gaming_learning <- gaming_learning %>%
  drop_na()

Display summary statistics.

summary(gaming_learning)

   student_id     gaming_hours   learning_score 
 Min.   : 1.00   Min.   : 2.00   Min.   :60.00  
 1st Qu.: 3.75   1st Qu.: 6.50   1st Qu.:69.00  
 Median : 6.50   Median :11.00   Median :78.50  
 Mean   : 6.50   Mean   :11.42   Mean   :77.83  
 3rd Qu.: 9.25   3rd Qu.:15.75   3rd Qu.:86.75  
 Max.   :12.00   Max.   :22.00   Max.   :94.00

5 Data Transformation

Create a learning performance category.

gaming_learning <- gaming_learning %>%
  mutate(
    performance_level = if_else(
      learning_score >= 80,
      "High",
      "Low"
    )
  )

head(gaming_learning)

  student_id gaming_hours learning_score performance_level
1          1            2             60               Low
2          2            4             64               Low
3          3            5             66               Low
4          4            7             70               Low
5          5            8             73               Low
6          6           10             77               Low

Count students in each performance category.

gaming_learning %>%
  count(performance_level)

  performance_level n
1              High 6
2               Low 6

6 Data Visualization

6.1 Scatter Plot

Visualize the relationship between gaming hours and learning score.

ggplot(
  gaming_learning,
  aes(
    x = gaming_hours,
    y = learning_score
  )
) +
  geom_point(size = 3) +
  labs(
    title = "Gaming Hours and Learning Performance",
    x = "Gaming Hours per Week",
    y = "Learning Score"
  )

6.2 Scatter Plot with Regression Line

ggplot(
  gaming_learning,
  aes(
    x = gaming_hours,
    y = learning_score
  )
) +
  geom_point(size = 3) +
  geom_smooth(
    method = "lm",
    se = TRUE
  ) +
  labs(
    title = "Relationship Between Gaming Hours and Learning Score",
    x = "Gaming Hours per Week",
    y = "Learning Score"
  )

7 Modeling

7.1 Simple Linear Regression

Fit a simple linear regression model.

model <- lm(
  learning_score ~ gaming_hours,
  data = gaming_learning
)

model


Call:
lm(formula = learning_score ~ gaming_hours, data = gaming_learning)

Coefficients:
 (Intercept)  gaming_hours  
      58.203         1.719

7.2 Model Summary

summary(model)


Call:
lm(formula = learning_score ~ gaming_hours, data = gaming_learning)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0307 -1.2086 -0.1961  1.2734  2.0053 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  58.20311    0.91795   63.41 2.32e-14 ***
gaming_hours  1.71944    0.07056   24.37 3.09e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.525 on 10 degrees of freedom
Multiple R-squared:  0.9834,    Adjusted R-squared:  0.9818 
F-statistic: 593.8 on 1 and 10 DF,  p-value: 3.087e-10

8 Interpretation of Results

The coefficient for gaming_hours shows the expected change in learning score for each additional hour of gaming per week.

A positive coefficient suggests that students who spend more time gaming tend to achieve higher learning scores.

The p-value helps determine whether the relationship is statistically significant.

9 Conclusion

This analysis demonstrates the complete data science workflow using a gaming-related research topic.

The workflow includes:

Research Design
Data Import
Data Cleaning
Data Transformation
Data Visualization
Modeling
Communication of Results

The simple linear regression model helps determine whether gaming hours are associated with learning performance.

10 Communication (Rendering Output)

To generate the final report:

Save this file as gaming_learning_workflow.qmd
Open it in RStudio
Click Render
Quarto will execute all code chunks
An HTML report will be generated automatically

The rendered report will contain:

Research Question
Hypothesis
Imported Data
Summary Statistics
Scatter Plots
Regression Results
Interpretation
Conclusion