Analytics Types & Visualization

Learning Analytics — Analytics & Visualization (Required)

Author

Luke Green

Published

June 18, 2026

Learning objectives

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

  • Simulate and save an educational dataset in R
  • Apply descriptive analytics using colMeans() and rowMeans()
  • Reshape data from wide to long format using pivot_longer()
  • Create and interpret scatter plots, bar plots, line plots, and histograms
  • Compute and interpret correlation between two variables
  • Apply the analytics type (descriptive, diagnostic, predictive) to real questions

The analytics types — a reminder

Before coding, connect each technique to the type of question it answers:

Analytics type Question Technique used in this file
Descriptive What happened? Summary stats, bar plot, histogram
Diagnostic Why did it happen? Scatter plot, correlation
Predictive What will happen next? Regression line, risk flagging

Keep this table in mind as you work through the exercises below. Every output you produce should be connected to one of these questions.

Part 1 · Creating and saving a simulated dataset

Instead of loading existing data, we will create our own simulated dataset. This teaches you how data is structured in R — useful when you need to build a small dataset from scratch for testing or teaching.

Creating the dataset

# set.seed() makes the random data reproducible —
# everyone running this code gets the same values
set.seed(42)

data_lms <- data.frame(
  Student_ID = paste("Student", 1:40, sep = "_"),
  Week_1  = sample(6:20, 40, replace = TRUE),
  Week_2  = sample(6:20, 40, replace = TRUE),
  Week_3  = sample(6:20, 40, replace = TRUE),
  Week_4  = sample(6:20, 40, replace = TRUE),
  Week_5  = sample(6:20, 40, replace = TRUE),
  Week_6  = sample(6:20, 40, replace = TRUE),
  Week_7  = sample(6:20, 40, replace = TRUE),
  Week_8  = sample(6:20, 40, replace = TRUE),
  Week_9  = sample(6:20, 40, replace = TRUE),
  Week_10 = sample(6:20, 40, replace = TRUE),
  Week_11 = sample(6:20, 40, replace = TRUE),
  Week_12 = sample(6:20, 40, replace = TRUE),
  Week_13 = sample(6:20, 40, replace = TRUE),
  Week_14 = sample(6:20, 40, replace = TRUE),
  Week_15 = sample(6:20, 40, replace = TRUE),
  Week_16 = sample(6:20, 40, replace = TRUE)
)

# Inspect the first few rows
head(data_lms)

Question: What does sample(6:20, 40, replace = TRUE) do? What would change if you set replace = FALSE? As always, use your own words to answer the question.

  • [It randomly generates 40 samples of outputs from 6 through 20. replace = TRUE allows numbers to repeat. replace = FALSE means no numbers can repeat. That would limit our number of possible generated samples to be a maximum of 15 (6 through 20)]

Saving the dataset

# Save as a CSV file in your project folder
write.csv(data_lms, "40_students_LMS_time_spent.csv", row.names = FALSE)

# Confirm it saved — check your Files pane for the new file

Question: Why is it important to be able to create and save datasets manually, rather than only working with provided data?

  • [In the real world, we will be creating and saving data on our own. It is optimistic to think that we will have the data organized and ready to go without having to build something the program receives. Also, it is a good idea to keep separate drafts in case you mess something up along the way]

Part 2 · Descriptive analytics — what happened?

Summary statistics

# Summary of all weekly columns (excluding Student_ID column)
summary_stats <- summary(data_lms[, -1])
summary_stats
     Week_1          Week_2          Week_3          Week_4     
 Min.   : 6.00   Min.   : 6.00   Min.   : 6.00   Min.   : 6.00  
 1st Qu.: 9.00   1st Qu.: 8.00   1st Qu.: 9.75   1st Qu.:12.00  
 Median :13.00   Median :10.50   Median :12.50   Median :14.50  
 Mean   :12.32   Mean   :10.75   Mean   :12.53   Mean   :14.28  
 3rd Qu.:15.00   3rd Qu.:13.00   3rd Qu.:16.00   3rd Qu.:18.00  
 Max.   :20.00   Max.   :20.00   Max.   :19.00   Max.   :20.00  
     Week_5          Week_6          Week_7          Week_8     
 Min.   : 6.00   Min.   : 6.00   Min.   : 6.00   Min.   : 6.00  
 1st Qu.: 8.50   1st Qu.: 9.00   1st Qu.: 8.75   1st Qu.:10.75  
 Median :13.50   Median :15.00   Median :14.00   Median :13.50  
 Mean   :13.18   Mean   :13.22   Mean   :13.60   Mean   :13.05  
 3rd Qu.:17.00   3rd Qu.:17.00   3rd Qu.:18.25   3rd Qu.:16.00  
 Max.   :20.00   Max.   :20.00   Max.   :20.00   Max.   :20.00  
     Week_9         Week_10         Week_11         Week_12     
 Min.   : 6.00   Min.   : 6.00   Min.   : 6.00   Min.   : 6.00  
 1st Qu.:10.00   1st Qu.: 9.00   1st Qu.:10.75   1st Qu.: 8.00  
 Median :14.00   Median :13.00   Median :14.00   Median :12.00  
 Mean   :13.05   Mean   :12.75   Mean   :13.47   Mean   :12.12  
 3rd Qu.:16.00   3rd Qu.:16.00   3rd Qu.:17.00   3rd Qu.:15.00  
 Max.   :20.00   Max.   :20.00   Max.   :20.00   Max.   :19.00  
    Week_13         Week_14         Week_15         Week_16     
 Min.   : 6.00   Min.   : 6.00   Min.   : 6.00   Min.   : 6.00  
 1st Qu.: 9.75   1st Qu.:10.75   1st Qu.:10.75   1st Qu.:10.00  
 Median :14.00   Median :14.00   Median :15.00   Median :15.00  
 Mean   :12.90   Mean   :13.70   Mean   :13.75   Mean   :13.65  
 3rd Qu.:16.00   3rd Qu.:17.25   3rd Qu.:17.00   3rd Qu.:17.00  
 Max.   :20.00   Max.   :20.00   Max.   :20.00   Max.   :20.00

Question: What insights do you gain from the summary? Pick one week and describe what the min, median, and max values tell you about student engagement that week.

  • [Week_14, 6 min says the lowest student had 6, 14 median says 50% of the class was below/above 14, 20 max says the highest student had 20]

Average time spent per week (colMeans)

# Select only the Week columns explicitly using grep()
# This protects against any extra columns added later (Semester_Average etc.)
# that would break names(average_time) if included accidentally
week_cols    <- grep("^Week_", names(data_lms), value = TRUE)
average_time <- colMeans(data_lms[, week_cols])
average_time
 Week_1  Week_2  Week_3  Week_4  Week_5  Week_6  Week_7  Week_8  Week_9 Week_10 
 12.325  10.750  12.525  14.275  13.175  13.225  13.600  13.050  13.050  12.750 
Week_11 Week_12 Week_13 Week_14 Week_15 Week_16 
 13.475  12.125  12.900  13.700  13.750  13.650

Question: If some weeks show notably higher or lower average time, what actions might an instructor take?

  • [The instructor might check in on a student who has declined in engagement after showing strong commitments previously. The instructor may also take into consideration the pacing and structure of the coursework to either include more content in lower weeks or move content away from the busier weeks.]

Each student’s semester average (rowMeans)

# rowMeans() calculates the mean across columns for each row (each student)
data_lms$Semester_Average <- rowMeans(data_lms[, 2:17])

head(data_lms |> select(Student_ID, Semester_Average))

Task: Calculate the average time spent for only Weeks 1–5 and save it as early_semester_average. Add it to the data frame.

# YOUR CODE HERE
early_week_cols    <- grep("^Week_[1-5]$", names(data_lms), value = TRUE)
early_semester_average <- colMeans(data_lms[, early_week_cols])
early_semester_average
Week_1 Week_2 Week_3 Week_4 Week_5 
12.325 10.750 12.525 14.275 13.175 
# Hint: weeks 1–5 are columns 2–6 in the data frame.
# Follow the same pattern as the row-means chunk above,
# but change the column range to cover only the first 5 weeks.

Question: How could the early semester average help an instructor identify at-risk students before midterm?

  • [Students who are far above or below the average time in the early weeks may require intervention. This can show that the learner is struggling to make time for the class or the learner is struggling to make sense of the material. ]

Part 3 · Visualization — bar plot and line plot

Prepare data for plotting

# Confirm average_time exists and has names before reshaping
# This prevents the "zero-length variable name" error
stopifnot(
  "Run the col-means chunk first" = exists("average_time"),
  "average_time has no names"     = !is.null(names(average_time)),
  "average_time is empty"         = length(average_time) > 0
)

average_time_table <- data.frame(
  Week               = factor(names(average_time), levels = names(average_time)),
  Average_Time_Spent = average_time
)

# Quick check — should show 16 rows, one per week
nrow(average_time_table)
[1] 16
head(average_time_table)

Bar plot — average time per week

ggplot(average_time_table, aes(x = Week, y = Average_Time_Spent)) +
  geom_bar(stat = "identity", fill = "#1D9E75", color = "white") +
  labs(
    title = "Average Time Spent per Week",
    x = "Week",
    y = "Average Hours"
  ) +
  theme_minimal() +
  theme(
    plot.title  = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Average LMS time spent per week across all 40 students

Line plot — trend over time

ggplot(average_time_table, aes(x = Week, y = Average_Time_Spent, group = 1)) +
  geom_line(color = "#185FA5", linewidth = 1.2) +
  geom_point(color = "#185FA5", size = 3) +
  labs(
    title = "Trend of Average Time Spent per Week",
    x = "Week",
    y = "Average Hours"
  ) +
  theme_minimal() +
  theme(
    plot.title  = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Trend of average LMS time across the semester

Question: What differences do you notice between the bar plot and the line plot? Which is more effective for showing a trend and why? Use your own words.

  • [The biggest difference I notice is the scale on the y axis representing Average Hours. The line plot is more “zoomed in” which highlights the differences in a more visually appealing way. Additionally, being able to visualize the rate of change (slope) of the line from one week to another highlights weeks that are different from one another.]

Line plot — individual students

# Reshape from wide to long format for individual student lines
data_long <- data_lms |>
  pivot_longer(
    cols      = starts_with("Week"),
    names_to  = "Week",
    values_to = "TimeSpent"
  )

ggplot(data_long, aes(x = Week, y = TimeSpent,
                      group = Student_ID, color = Student_ID)) +
  geom_line(alpha = 0.5) +
  labs(
    title = "Weekly Time Spent by Each Student",
    x = "Week",
    y = "Hours"
  ) +
  theme_minimal() +
  theme(
    plot.title     = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.text.x    = element_text(angle = 45, hjust = 1),
    legend.position = "none"
  )

Weekly LMS time for all 40 students

Question: What patterns do you notice when looking at all 40 students at once? Is this visualization easy to interpret? Why or why not?

  • [This seems near impossible! Not easy for me to interpret whatsoever because there is so much variance from student to student and the fact that there are 40 lines crammed on this single chart]

Line plot — selected students only

Task: Choose 5 students you want to compare and update the code below.

# YOUR CODE HERE
#| label: lineplot-five
#| fig-cap: "Weekly LMS time for 5 random students"

data_long <- data_lms |> filter(Student_ID %in% c("Student_1", "Student_6","Student_11", "Student_16", "Student_21")) |>
  pivot_longer(
    cols      = starts_with("Week"),
    names_to  = "Week",
    values_to = "TimeSpent"
  )
ggplot(data_long, aes(x = Week, y = TimeSpent,
                      group = Student_ID, color = Student_ID)) +
  geom_line(alpha = 0.5) +
  labs(
    title = "Weekly Time Spent of Five Students",
    x = "Week",
    y = "Hours"
  ) +
  theme_minimal() +
  theme(
    plot.title     = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.text.x    = element_text(angle = 45, hjust = 1),
    legend.position = "none")

Weekly LMS time for selected students
# Step 1: Choose 5 Student_IDs from the data and filter for them.
#         Student IDs are in the format "Student_1", "Student_2", etc.
#         Pick students whose patterns you find interesting to compare —
#         for example, mix high and low average engagement.
#
# Step 2: Reshape with pivot_longer() — same as the lineplot-all chunk above.
#
# Step 3: Plot with ggplot() — copy the structure from lineplot-all
#         and adjust the title and legend position.

Question: What insights do you gain from this focused view? What design decisions did you make in choosing these five students?

  • [This focused view allows me to actually see the trend of individual students compared to one another as compared to the previous chart that had 40 students plotted making it possible to actually compare students with one another. You can now identify weeks where more of these five students increased or decreased participation. You could identify weeks where most students are spending more or less time. You can also notice that there is variablility from week to week and from student to student that can not always be explained by the content]

Histogram — semester averages

ggplot(data_lms, aes(x = Semester_Average)) +
  geom_histogram(binwidth = 1, fill = "#378ADD", color = "white") +
  labs(
    title = "Distribution of Semester Average Time Spent",
    x = "Semester Average (hours/week)",
    y = "Number of Students"
  ) +
  theme_minimal() +
  theme(plot.title = element_text(size = 14, face = "bold", hjust = 0.5))

Distribution of semester averages across 40 students

Part 4 · Diagnostic analytics — why did it happen?

Now we switch to the sci-online-classes dataset to explore the relationship between time spent and final grades.

# Load the dataset used in the previous module
# Make sure sci-online-classes.csv is in your data folder
data_sci <- read_csv("data/sci-online-classes.csv") |>
  clean_names()

glimpse(data_sci)
Rows: 603
Columns: 30
$ student_id            <dbl> 43146, 44638, 47448, 47979, 48797, 51943, 52326,…
$ course_id             <chr> "FrScA-S216-02", "OcnA-S116-01", "FrScA-S216-01"…
$ total_points_possible <dbl> 3280, 3531, 2870, 4562, 2207, 4208, 4325, 2086, …
$ total_points_earned   <dbl> 2220, 2672, 1897, 3090, 1910, 3596, 2255, 1719, …
$ percentage_earned     <dbl> 0.6768293, 0.7567261, 0.6609756, 0.6773345, 0.86…
$ subject               <chr> "FrScA", "OcnA", "FrScA", "OcnA", "PhysA", "FrSc…
$ semester              <chr> "S216", "S116", "S216", "S216", "S116", "S216", …
$ section               <chr> "02", "01", "01", "01", "01", "03", "01", "01", …
$ gradebook_item        <chr> "POINTS EARNED & TOTAL COURSE POINTS", "ATTEMPTE…
$ grade_category        <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ final_grade_cems      <dbl> 93.45372, 81.70184, 88.48758, 81.85260, 84.00000…
$ points_possible       <dbl> 5, 10, 10, 5, 438, 5, 10, 10, 443, 5, 12, 10, 5,…
$ points_earned         <dbl> NA, 10.00, NA, 4.00, 399.00, NA, NA, 10.00, 425.…
$ gender                <chr> "M", "F", "M", "M", "F", "F", "M", "F", "F", "M"…
$ q1                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q2                    <dbl> 4, 4, 4, 5, 3, NA, 5, 3, 3, NA, NA, 5, 3, 3, NA,…
$ q3                    <dbl> 4, 3, 4, 3, 3, NA, 3, 3, 3, NA, NA, 3, 3, 5, NA,…
$ q4                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 3, 5, NA,…
$ q5                    <dbl> 5, 4, 5, 5, 4, NA, 5, 3, 4, NA, NA, 5, 4, 5, NA,…
$ q6                    <dbl> 5, 4, 4, 5, 4, NA, 5, 4, 3, NA, NA, 5, 3, 5, NA,…
$ q7                    <dbl> 5, 4, 4, 4, 4, NA, 4, 3, 3, NA, NA, 5, 3, 5, NA,…
$ q8                    <dbl> 5, 5, 5, 5, 4, NA, 5, 3, 4, NA, NA, 4, 3, 5, NA,…
$ q9                    <dbl> 4, 4, 3, 5, NA, NA, 5, 3, 2, NA, NA, 5, 2, 2, NA…
$ q10                   <dbl> 5, 4, 5, 5, 3, NA, 5, 3, 5, NA, NA, 4, 4, 5, NA,…
$ time_spent            <dbl> 1555.1667, 1382.7001, 860.4335, 1598.6166, 1481.…
$ time_spent_hours      <dbl> 25.91944500, 23.04500167, 14.34055833, 26.643610…
$ time_spent_std        <dbl> -0.18051496, -0.30780313, -0.69325954, -0.148446…
$ int                   <dbl> 5.0, 4.2, 5.0, 5.0, 3.8, 4.6, 5.0, 3.0, 4.2, NA,…
$ pc                    <dbl> 4.50, 3.50, 4.00, 3.50, 3.50, 4.00, 3.50, 3.00, …
$ uv                    <dbl> 4.333333, 4.000000, 3.666667, 5.000000, 3.500000…
Note

This is the same dataset from previous module. We are reloading it here because the LMS time data (Parts 1–3) and the sci-online-classes data (Part 4) are separate files. Reloading makes this file self-contained.

Scatter plot with regression line

ggplot(data_sci,
       aes(x = time_spent_hours, y = final_grade_cems)) +
  geom_point(color = "#185FA5", size = 2.5, alpha = 0.6) +
  geom_smooth(method = "lm", color = "#993C1D", se = TRUE) +
  labs(
    title = "Time Spent vs. Final Grade",
    x = "Time Spent on LMS (hours)",
    y = "Final Grade"
  ) +
  theme_minimal() +
  theme(plot.title = element_text(size = 14, face = "bold", hjust = 0.5))

Relationship between time spent and final grade

Question: Based on the scatter plot, what do you expect the relationship between time spent and final grades to be? Write your hypothesis before looking at the correlation.

  • [The relationship between time spent and final grades will be a strong-positive relationship]

Correlation

# cor() computes the Pearson correlation coefficient
# use = "complete.obs" ignores rows with missing data
correlation <- cor(data_sci$time_spent_hours,
                   data_sci$final_grade_cems,
                   use = "complete.obs")

correlation
[1] 0.3654121
TipInterpreting correlation
  • Values close to +1: strong positive relationship (more time → higher grade)
  • Values close to -1: strong negative relationship
  • Values close to 0: little or no linear relationship
  • This is NOT a statistics course — focus on interpreting what this number means for learners, not on p-values.

Question: With both the scatter plot and the correlation value in front of you, what can you say about the relationship between time spent and final grades? What would you recommend to an instructor based on this finding?

  • [There appears to be a weak-moderate positive relationship. While it is not strong, it is still showing a positive relationship between time spent and final grade. I would recommend an instructor encourage students to dedicate time to learning the material and share with students the data findings in a visually appealing and simple manner]

Practice — grouped summary by subject

Task: Using data_sci, calculate the mean final_grade_cems and mean time_spent_hours grouped by subject. Arrange by mean grade descending. Which subject has the highest average grade? Is it also the subject with the most time spent?

TipHint

You have used group_by() and summarise() in the previous file. Apply the same pattern here with a different grouping variable. If you need a column name reminder, run names(data_sci) in the Console.

# YOUR CODE HERE
data_sci %>%
  group_by(subject) %>%
  summarise(
    mean_final_grade = mean(final_grade_cems, na.rm = TRUE), 
    mean_time_spent = mean(time_spent_hours, na.rm = TRUE)
  ) %>%
  arrange(desc(mean_final_grade))
# Steps: group_by(subject) |> summarise(mean_grade = ..., mean_time = ...) |> arrange(desc(...))

Question: Does the subject with the highest average grade also have the most time spent? What might explain any differences you find?

  • [The subject with the highest average grade has the second least average hours spent. This could imply that the content of the course is much easier/shorter than the others.If BioA is a freshamn course, it could show that students have not yet learned the time management skills to succeed in a class because it has the least amount of average hours and the lowest average score ]

Part 5 · Box plot

A box plot shows the distribution of a variable across categories — useful for comparing groups and spotting outliers.

ggplot(data_sci, aes(x = gender, y = final_grade_cems, fill = gender)) +
  geom_boxplot(color = "gray30",
               outlier.colour = "#993C1D",
               outlier.shape  = 16,
               outlier.size   = 2) +
  scale_fill_manual(values = c("F" = "#E1F5EE", "M" = "#E6F1FB")) +
  labs(
    title = "Final Grade Distribution by Gender",
    x     = "Gender",
    y     = "Final Grade"
  ) +
  theme_minimal() +
  theme(
    plot.title     = element_text(size = 14, face = "bold", hjust = 0.5),
    legend.position = "none"
  )

Final grade distribution by gender

Question: What does the box plot tell you about the distribution of final grades by gender? Are there differences worth investigating?

  • [The distributions by gender are very similar. The females first quartile is slighly lower than the males, but their median and third quartile are nearly identical. Both groups appear to have left-skewed data with outliers in the less than 50 range. A correlation coefficient could help us better determine if there is justification for further investigating, but the boxplot leads me to believe there is no need.]

Final reflection

After completing both the LMS time analysis and the sci-online-classes analysis, reflect on the following:

Question: How could these analytics techniques be applied in a real classroom or course design context? Describe one specific scenario — from your track (K–12 or ID/higher ed) — where the combination of a bar plot, line plot, and correlation would help an educator or designer make a better decision.

  • [In my high school Algebra II class, I could use a bar plot, line plot, and correlation to analyze student behaviors compared with academic performance. I could compare unit test scores with homework completion rates uing a bar plot. I could use a line plot to show student averages from one unit to another. The correlation analysis would tell me if there is a strong relationship that shows two events tend to happen together.This lets me go through the process of determing not only what students need help with, but how and why students the students need help.]

Render & submit

Step 1 — Add your name

Change the author: field in the YAML header at the top to your name.

Step 2 — Render

Click Render in the toolbar. A formatted HTML page will appear in your Viewer tab or a new browser window. Check the Console for any error messages if the render fails.

Step 3 — Publish

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TipE-portfolio tip

This document shows three levels of analytics work: descriptive (summary statistics and bar plots), trend analysis (line plots), and diagnostic (scatter plot and correlation). Together they demonstrate a complete analytical workflow that is worth showcasing in a professional portfolio.

Share your published link with your instructor once you have rendered and published. Post in the course discussion board if you run into any technical issues.