Visualization Practices

CUED 7540: Learning Analytics III

Author

Haley Smith, hballen42@tntech.edu

Published

October 26, 2025

Learning Objectives

By the end of this lesson, you will be able to:

- Generate basic analytics plots using ggplot2.

- Customize plots for better data visualization.

- Interpret different types of visualizations to gain insights.

- Understand how to apply visualization to different data analysis tasks.

Part 1: Loading and Exploring Data**

Before we start creating plots, we need to load and inspect the datasets we’ll be working with. We’ll use a new dataset called educational_data.csv.

Task 1: Load the educational_data.csv dataset and inspect its structure. This will help you understand what variables you’re working with.

Reflect & Respond

Question 1: What Catches Your Eye? As you browse through the dataset, what stands out to you? Is there anything that piques your curiosity? Maybe a surprising trend or a pattern you didn’t expect?

  • [I immediately notice the relationship between hours studied and the grade. ]

Question 2: What Questions Do You Have? Is there something specific you’d like to dig deeper into? Think about what you might want to learn more about. Are there any relationships between variables you’re curious about?

  • [I want to know whether homework completion explains additional variation on quiz score once study hours is considered. I am also curious whether the study hours to quiz score relationship differs by gender.]

Question 3: What’s Your Analytics Game Plan? How would you approach analyzing this dataset? What steps would you take to uncover the insights you’re interested in?

  • [I will start with univariate summaries and plots to understand distributions. Next, I will fit scatter plots with trend lines and compute the correlations. Then I will arrange the data by Gender, and use a simple linear model with Quiz Score as the outcome and Study Hours plus Homework Completion as predictors.]

Part 2: Visualizing Relationship and Scatter Plots

Scatter plots are useful for visualizing the relationship between two continuous variables. The gg in ggplot stands for “Grammar of Graphics,” which means we build plots in layers.

Scatter Plot

Task 2: Create a scatter plot to explore the relationship between Study_hours and Quiz_Score. This plot will help you visualize if there’s a correlation.

# Create a scatter plot of Study_hours vs. Quiz_Score with a regression line

ggplot(data3, aes(x = Study_Hours, y = Quiz_Score)) + # TYPE YOUR CODE. TWO VARIABLES HERE
  geom_point(color = "blue", size = 3, alpha = 0.6) +
  geom_smooth(method = "lm", color = "red", se = TRUE) +  # This line will add a linear regression line
  labs(title = "Scatter Plot of Study Hours vs. Quiz_Score", #UPDATE YOUR PLOT TITLES
       x = "Study Hours (Hours)",
       y = "Quiz_Score") +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face = "bold")
  )
`geom_smooth()` using formula = 'y ~ x'

Task 3: Now that we’ve visualized the relationship, let’s compute the correlation between the two variables to get a numerical value for their relationship. The use = "complete.obs" argument handles any missing values by only using the rows that have data for both variables.

# Compute the correlation
# COMPLETE YOUR CODE BELOW
correlation <- cor(data3$Quiz_Score, data3$Study_Hours, use = "complete.obs")

# Display the correlation
correlation
[1] -0.04308071

Reflect & Respond

Question: What does the correlation value tell you about the relationship between study hours and quiz scores?

  • [I think I did something wrong, because I would think that a positive value would indicate that the two are related and more hours of study would lead to higher quiz scores, and the closer to 1 the stronger the relationship.]

Activity: Customize the Scatterplot

# Create a scatterplot of 'Quiz_Score' vs 'Final_Exam_Score'
ggplot(data3, aes(x = Quiz_Score, y = Final_Exam_Score)) +
  geom_point(size = 3, alpha = 0.7, color = "red") +
  labs(
    title = "Scatter Plot of Quiz Score vs Final Exam Score",
    x = "Quiz Score",
    y = "Final Exam Score"
  ) +
  theme_minimal()

Let’s customize the relationship between Quiz_Score and ‘Final_Exam_Score’.

  1. Change the size of the points to make them more prominent. You can also experiment with different shapes (e.g., circles, triangles, squares) Hint: add ‘size = 3, shape = 16’ inside geom_point(). Numbers can change.

  2. Add a linear regression line to your scatter plot to see the trend between variable 1 and variable 2. *Hint: Use geom_smooth()

  3. Update the title and the axis labels. Make the title bold and center it.

  4. Use facet_wrap() to create separate scatter plots based on Gender. *Hint:facet_wrap()

  5. Exercise with the {scatterplot-activity} chunk below.

# Create a scatterplot of 'Quiz_Score' vs 'Final_Exam_Score'
# COMPLETE THE CODE
ggplot(data3, aes(x = Quiz_Score, y = Final_Exam_Score)) +
  geom_point(color = "purple", size = 3, shape = 16) +
  geom_smooth(method = "lm", color = "lightpink", se = FALSE) +
  labs(
    title = "Scatterplot of Quiz Score vs Final Exam Score",
    x = "Quiz Score",
    y = "Final Exam Score"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title.x = element_text(size = 12, face = "bold"),
    axis.title.y = element_text(size = 12, face = "bold")
  )
`geom_smooth()` using formula = 'y ~ x'

Part 3: Histogram

Histograms are used to visualize the distribution of a single continuous variable. Let’s create a histogram of Homework_completion.

ggplot(data3, aes(x = Homework_Completion)) +
  geom_histogram(binwidth = 5, fill = "blue", color = "black", na.rm = TRUE) +
  labs( title = "Histogram of Homework Completion",
    x = "Homework Completion (%)",
    y = "Frequency" )

Activity: Customize the Histogram

  1. Change the fill and color of the bars to something else. You can choose any colors you like! Use the link provided earlier for color options. color names

  2. Change the binwidth, and observe how the histogram changes. What happens if you set it to different numbers?

  3. Update the title and the x-axis to something that is relevant to your analytics.

  4. Apply the theme_minimal() and see how it changes the look of your plot. Try out other themes like theme_classic() or theme_dark(). *Hint - You can add +theme_minimal() at the end of the code line.

  5. Add a vertical line at the mean of the variable to highlight the average value. Use geom_vline(). The mean() function with na.rm = TRUE will ignore missing values.

  6. Use facet_wrap() to create separate scatterplots ffor a categorical variable like Gender. For example, if you want two scatterplots based on ‘Gender’ variable, the syntax is +facet_wrap(~Gender).

  7. Use the histogram-activity chunk below to practice.

# Customize the histogram of the 'Homework_completion' variable
# COMPLETE THE CODE
ggplot(data3, aes(x = Homework_Completion)) +
  geom_histogram(binwidth = 3, fill = "lightblue", color = "black", na.rm = TRUE) +
  geom_vline(aes(xintercept = mean(Homework_Completion, na.rm = TRUE)),
             linetype = "dashed", linewidth = 1, color = "red") +
  labs(
    title = "Distribution of Homework Completion",
    x = "Homework Completion (%)",
    y = "Frequency"
  ) +
  theme_dark() +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

Part 4: Exploring Grouped Data with Box Plots

Boxplots are useful for visualizing the distribution of a variable across different categories and identifying potential outliers.

Box Plot

Let’s create a box plot of Homework_Completionby Gender.

# Create a boxplot of 'Homework_Completion' by 'Gender'
# COMPLETE THE CODE
ggplot(data3, aes(x = Gender, y = Homework_Completion, fill = Gender)) +
  geom_boxplot() +
  labs(title = "Boxplot of Homework Completion by Gender", x = "Gender", y = "Homework Completion (%)") + theme_minimal()

Activity: Customize the Boxplot

  1. Change the fill colors for the boxes.

  2. Add color = “black” (or some other color) to the geom_boxplot() to set the color of the box outlines.

  3. Update the title and the y-axis label properly. Make the title bold and center it. **Hint: ** Add +theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

  4. Customize the outliers by changing their shape and color. For example, make outliers larger and red by adding +geom_boxplot(outlier.colour = "red", outlier.shape = 16, outlier.size = 3)

  5. Use the boxplot-activity chunk below to practice.

# Create a boxplot of 'Homework_Completion' by 'Gender'
# COMPLETE THE CODE
ggplot(data3, aes(x = Gender, y = Homework_Completion, fill = Gender)) +
  geom_boxplot(color = "black",
               outlier.colour = "red", outlier.shape = 16, outlier.size = 3) +
  labs( title = "Homework Completion by Gender",
    x = "Gender",
    y = "Homework Completion (%)" ) +theme_minimal() + theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.position = "none")

Part 5: Counting Categories with Bar Plots

Bar plots can display the counts of different categories in your data.

Task 5: Visualize the count of students by Gender.

# Create a bar plot of counts of 'Gender'
# COMPLETE THE CODE BELOW
ggplot(data3, aes(x = Gender)) +
  geom_bar(fill = "green", color = "black") +
  labs(title = "Bar Plot", x = "Gender", y = "Count")

Activity: Customize the Bar Plot

  1. Change the fill color of the bars.

  2. Change the width of the bars by using the width parameter inside geom_bar(). (i.e., width = 0.5)

  3. Update the title and the y-axis labels to be descriptive. Make the title bold and center it.

  4. Use the barplot-activity chunk below to practice.

# Create a bar plot of counts of 'Gender'
# COMPLETE THE CODE
ggplot(data3, aes(x = Gender)) + 
  geom_bar(width = 0.6 , fill = "black", color = "black") +
  labs(title = "Bar Plot of Students by Gender", x = "Gender", y = "Count")+
  geom_bar(aes(fill = Gender), color = "black") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_fill_manual(values = c("F" = "plum", "M" = "steelblue"))+
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5))

Final Reflection : How can we use LA in instructional design and decision?

After practicing the basic analysis and visualization techniques for the past couple of weeks, take some time to reflect on how these skills can be applied in the real world.

  1. Consider the role of an educator, instructional designer, curriculum developer, policymaker, etc. How might the ability to analyze and visualize data help you make informed decisions, improve learning outcomes, or design more effective educational experiences? Think broadly about the implications of these skills in your current or future professional context, and share your thoughts on how data-driven insights could enhance your work.
  • [Analyzing and visualizing data provides educators and instructional designers with the ability to make informed, evidence-based decisions rather than relying solely on intuition. These insights allow for targeted interventions that can improve learning outcomes and equity across classrooms. From an instructional design perspective, learning analytics supports continuous improvement. Visualizing engagement data helps designers evaluate whether certain activities truly foster participation or if adjustments are needed. For policy makers, data visualization make inequities more apparent and encourage trasnparency.]

Render & Submit

Congratulations, you’ve completed the module!

To receive full score, you will need to render this document and publish via a method such as: Quarto Pub, Posit Cloud, RPubs , GitHub Pages, or other methods. Once you have shared a link to you published document with me and I have reviewed your work, you will be officially done with the current module.

Complete the following steps to submit your work for review by:

  1. First, change the name of the author: in the YAML header at the very top of this document to your name. The YAML header controls the style and feel for knitted document but doesn’t actually display in the final output.

  2. Next, click the “Render” button in the toolbar above to “render” your R Markdown document to a HTML file that will be saved in your R Project folder. You should see a formatted webpage appear in your Viewer tab in the lower right pan or in a new browser window. Let me know if you run into any issues with rendering.

  3. Finally, publish. To do publish, follow the step from the link

If you have any questions about this module, or run into any technical issues, don’t hesitate to contact me.

Once I have checked your link, you will be notified!