Advanced Visualization Techniques

Learning Analytics — Week 4 (Required)

Author

Cassidy Newby

Published

October 8, 2026

Learning objectives

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

  • Create a word cloud from text data and interpret frequency patterns
  • Build a heatmap to reveal patterns across two dimensions
  • Visualize a social network of student interactions using SNA
  • Reflect on when each visualization type is the most appropriate choice

When to use which advanced visualization

Visualization Best for Typical data source
Word cloud Frequency of words in text Survey responses, discussion posts, feedback
Heatmap Patterns across two categories Scores by subject + week, engagement by module + cohort
SNA plot Relationships between individuals Forum reply data, collaboration records

Before you run any code, read through the whole file once. Notice which dataset each part uses and make sure the file is in your data folder.

Part 1 · Word cloud

A word cloud visualizes how often words appear in a text — larger words appear more frequently. We will use the Handbook of Learning Analytics (2022), which has been converted to a plain text file.

Packages for this section

# Text mining packages
# install.packages(c("tm", "wordcloud")) if you get errors here
library(tm)
library(wordcloud)

Load the text data

# read.delim() reads a plain text file
# header = FALSE and stringsAsFactors = FALSE read it as raw text
la_text <- read.delim("data/Handbook of LA.txt",
                      header = FALSE,
                      stringsAsFactors = FALSE)

# Create a text corpus — a collection of text documents the tm package can process
doc <- Corpus(VectorSource(la_text))

head(doc)
<<SimpleCorpus>>
Metadata:  corpus specific: 1, document level (indexed): 0
Content:  documents: 1

Clean the text

Before generating the word cloud, we remove common words, punctuation, numbers, and other noise. This is called text preprocessing.

# Helper function: replace specific characters with a space
toSpace <- content_transformer(function(x, pattern) gsub(pattern, " ", x))

doc <- doc |>
  (\(d) {
    d <- tm_map(d, toSpace, "/")
    d <- tm_map(d, toSpace, "@")
    d <- tm_map(d, toSpace, "\\|")
    d <- tm_map(d, content_transformer(tolower))
    d <- tm_map(d, removeWords,
                c(stopwords("english"), "https", "can", "doi", "also", "use", "students", "learning"))
    d <- tm_map(d, removeNumbers)
    d <- tm_map(d, removePunctuation)
    d <- tm_map(d, stripWhitespace)
    d
  })()
Note

You may see warning messages when running the preprocessing chunk. These are expected from the tm package — as long as you see no error messages, you can continue.

Generate the word cloud

# Build a term-document matrix — a table of word frequencies
dtm <- TermDocumentMatrix(doc)
m   <- as.matrix(dtm)
v   <- sort(rowSums(m), decreasing = TRUE)
word_freq <- data.frame(word = names(v), freq = v)

# Top 10 most frequent words
head(word_freq, 10)

Word cloud from the Handbook of Learning Analytics (2022)

# Generate the word cloud
set.seed(1234)
wordcloud(
  words       = word_freq$word,
  freq        = word_freq$freq,
  min.freq    = 10,
  max.words   = 50,
  random.order = FALSE,
  rot.per     = 0.35,
  colors      = brewer.pal(8, "Dark2")
)

Word cloud from the Handbook of Learning Analytics (2022)

Task 1 — Parameter exploration: Try changing max.words to 50 or 150 and min.freq to 10. How does the word cloud change? Which version is more readable?

  • [One change it make when changing it to 50 is the fact there is less words, and 150 is more words. I believe 50 is more readable because the more important words are a lot more visible than at 150.]

Task 2 — Add your own stopwords: Look at the word cloud above. Find 2–3 words that appear large but feel too generic to be meaningful — words that are frequent only because they appear in every chapter, not because they signal a specific theme.

Add those words to the removeWords() line in the text-preprocessing chunk above, then re-run that chunk and the wordcloud-generate chunk. The line to edit looks like this:

d <- tm_map(d, removeWords,
            c(stopwords("english"), "https", "can", "doi", "also", "use"))

Add your words inside the c(...) vector, then re-run both chunks.

Reflection: Which words did you remove? Why did you choose them? Does the revised cloud reveal any themes the original missed?

  • [I removed the word students and learning. Learning was the main big word, so I wanted to see what it would reveal next and that was analytics. I guess the theme is about research and data, but I am not exactly sure.]

Question: Looking at the most frequent words, what does this tell you about the themes the learning analytics field focuses on? Does anything surprise you?

  • [No, it does not surprise me that those are the most frequent words. The themes tells me that links and sources come up a lot in research. ]

Part 2 · Heatmap

A heatmap uses color intensity to show values across two categorical dimensions — useful for spotting patterns that would be invisible in a table or bar chart.

Package for this section

# reshape2 gives us melt() for converting wide to long format
# install.packages("reshape2") if needed
library(reshape2)

Load the data

data_hm <- read.csv("data/student_assignment_scores.csv")

head(data_hm)
glimpse(data_hm)
Rows: 30
Columns: 11
$ Student_ID    <chr> "Student_1", "Student_2", "Student_3", "Student_4", "Stu…
$ Assignment_1  <int> 98, 86, 50, 74, 59, 85, 67, 98, 96, 73, 56, 85, 74, 86, …
$ Assignment_2  <int> 77, 89, 52, 82, 91, 73, 79, 92, 64, 71, 57, 85, 53, 71, …
$ Assignment_3  <int> 52, 70, 51, 87, 59, 89, 54, 82, 98, 88, 85, 94, 91, 58, …
$ Assignment_4  <int> 98, 67, 54, 95, 89, 89, 70, 85, 85, 88, 91, 67, 76, 62, …
$ Assignment_5  <int> 54, 58, 65, 75, 92, 91, 91, 82, 78, 74, 100, 81, 66, 95,…
$ Assignment_6  <int> 87, 80, 55, 69, 64, 83, 91, 72, 61, 75, 90, 50, 51, 73, …
$ Assignment_7  <int> 54, 84, 63, 63, 77, 82, 86, 84, 75, 80, 94, 92, 81, 66, …
$ Assignment_8  <int> 80, 57, 65, 52, 86, 62, 61, 50, 79, 100, 80, 86, 96, 55,…
$ Assignment_9  <int> 82, 87, 92, 90, 71, 75, 69, 94, 86, 92, 64, 70, 83, 74, …
$ Assignment_10 <int> 79, 54, 71, 69, 99, 90, 60, 77, 87, 91, 58, 72, 75, 100,…

Reshape and plot

# melt() converts wide format (one column per assignment) to long format
# (one row per student-assignment combination) — required for ggplot heatmaps
data_hm_long <- melt(data_hm,
                     id.vars      = "Student_ID",
                     variable.name = "Assignment",
                     value.name   = "Score")

ggplot(data_hm_long,
       aes(x = Assignment, y = Student_ID, fill = Score)) +
  geom_tile(color = "white", linewidth = 0.3) +
  scale_fill_gradient(low = "#F0FAF6", high = "#0F6E56") +
  labs(
    title = "Heatmap of Student Progress Across Assignments",
    x     = "Assignment",
    y     = "Student ID",
    fill  = "Score"
  ) +
  theme_minimal() +
  theme(
    plot.title  = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.text.y = element_text(size = 8)
  )

Student scores across assignments — darker = higher score

Task: Copy the ggplot() call from the heatmap-plot chunk above into the blank chunk below. Make two changes:

  1. Change the low and high colors in scale_fill_gradient() — for example: low = "white", high = "#993C1D" (red gradient).
  2. Update the x and y labels in labs() to something more descriptive than "Assignment" and "Student ID".
TipHint

Copy the entire ggplot(data_hm_long, ...) block from above. You only need to change two things: scale_fill_gradient() and labs(). Everything else stays the same.

# YOUR CODE HERE — copy the ggplot() call above and apply your two changes

ggplot(data_hm_long,
       aes(x = Assignment, y = Student_ID, fill = Score)) +
  geom_tile(color = "white", linewidth = 0.3) +
  scale_fill_gradient(low = "white", high = "#993C1D") +
  labs(
    title = "Heatmap of Student Progress Across Assignments",
    x     = "Assignments",
    y     = "Student",
    fill  = "Score"
  ) +
  theme_minimal() +
  theme(
    plot.title  = element_text(size = 14, face = "bold", hjust = 0.5),
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.text.y = element_text(size = 8)
  )

Heatmap with custom colors and labels

Reflection: How does the color change affect how you interpret the data? Does the red gradient feel different from the green one — and why might that matter when presenting to a principal or department chair?

  • [I like the green and white gradient a lot better. The red makes me feel like everything is negative or bad, and red is a color that I cannot see well anyway. Similar to me, a principal or a department chair might see green and think more postive versus red and think all bad thoughts, or they might need a different color because of colorblindness.]

Question: Looking at the heatmap, identify one student and one assignment that stand out. What would you do as an instructor based on what you see?

  • [Assignment 2 and student 30, based on the assignment scores being low I would think about redoing or allowing the students to make corrections based on what it was. If it is an instruction flaw, I would replace the assignment grade and reteach the subject, but if it is an individual student struggle I would pull them out in small groups like student 30 to focus on what they need to relearn.]

Part 3 · Social network analysis (SNA)

Social network analysis maps relationships and interactions between people. In education, this reveals who is connected, who is isolated, and who acts as a hub or bridge in a learning community.

Package for this section

# igraph is the main package for network analysis in R
# install.packages("igraph") if needed
library(igraph)

Load the interaction data

# This dataset has two columns: From and To
# Each row is one interaction (e.g., Student_1 replied to Student_5)
data_sna <- read.csv("data/student_interactions.csv")

head(data_sna)

Build the network

# Convert the data frame to a graph object
# directed = FALSE means interactions go both ways
g <- graph_from_data_frame(d = data_sna, directed = FALSE)

# Summary of the network
summary(g)
IGRAPH e903e03 UNW- 30 100 -- 
+ attr: name (v/c), weight (e/n)

Basic network plot

plot(
  g,
  vertex.label = V(g)$name,
  vertex.size  = 30,
  vertex.color = "#E1F5EE",
  edge.color   = "gray60",
  main         = "Student Interaction Network"
)

Basic social network of student interactions

Enhanced network — node size by connections

# Compute degree — how many connections each student has
V(g)$degree <- degree(g)

# Color palette from RColorBrewer (loaded in the main setup chunk)
n_nodes       <- length(V(g))
node_colors   <- brewer.pal(min(n_nodes, 12), "Set3")

plot(
  g,
  vertex.label       = V(g)$name,
  vertex.size        = V(g)$degree * 1.5 + 5,   # bigger = more connected
  vertex.color       = node_colors[seq_len(n_nodes) %% length(node_colors) + 1],
  edge.color         = "gray60",
  vertex.label.cex   = 0.6,
  vertex.label.color = "#2C2C2A",
  layout             = layout_with_fr,
  main               = "Student Interaction Network — Node Size = Degree"
)

Network with node size scaled by number of connections (degree)
TipClick “Show in New Window” for a better view

Network plots can be hard to read in the small viewer. Click the expand icon in the top right of the plot pane to open it full size.

Task: The enhanced plot above shows node size by degree, but a visual ranking is hard to read precisely when many nodes are close in size. The chunk below has already built a data frame called degree_df with each student’s name and degree count. Your job is to sort it and find the top 3 most-connected students.

# This data frame is already built for you from the sna-enhanced chunk above.
# V(g)$name = student names, V(g)$degree = number of connections each has
degree_df <- data.frame(
  student = V(g)$name,
  degree  = V(g)$degree
)

# YOUR CODE HERE
# Use arrange() and head() to show the top 3 students by degree, descending.
# Hint: arrange(desc(degree)) |> head(3)

degree_df |>
  arrange(desc(degree)) |>
  head(3)

Reflection: Does the result match what you expected from the visual? Which student is most connected and what does that mean for an instructor?

  • [For the most part, they do match what I expected from the visual. I was thinking maybe Christopher would be on the list. David is the student most connected, which means that he is a good student to possible put in a group of less interactive students to help them out. It also means that he is a good leader and student to call on when needed.]

Question: Looking at the enhanced network plot, identify:

  1. Which students appear most central (largest nodes)? What does that mean?
  2. Are any students isolated (small nodes with few or no connections)?
  3. What would you do as an instructor based on what you see?
  • [1. David, Mia, and Emily it means that these students are involved in class and have the most connections with other students.
  1. James and Joshua appear to be on the outside of the classroom.
  2. As a teacher, I would try to pair David up with James and Joshua to see if I could get them to come out of their shells. ]

Part 4 · Visualization choice reflection

You have now used five visualization types: scatter plot, bar plot, line plot, histogram, word cloud, heatmap, and SNA plot (this week).

Question 1: For each of the three advanced techniques, describe one specific scenario from your track (K–12 or ID/higher ed) where it would be the most useful choice:

  • Word cloud: [I would have every student submit two word description of their biggest challenge they faced within their project. I would them project the words on the board to show the other students that they have alike problems.]
  • Heatmap: [In a Heatmap, I would gather data to show students their missing work from across the semester allowing them to see where they need to catch up before grades are due.]
  • SNA plot: [I would use this one if I was a STEM coordinator to build a map of all the things that connect STEM teachers in the area to industrial partners. This would show teachers how to better connect with businesses in the area to help out their students. ]

Question 2: What challenges might you face implementing these visualizations in a real school or institutional setting? Think about data availability, privacy, and stakeholder interpretation.

  • [In my classroom, I do not believe it would be any problem since I am using data that I would use anyways. Additionally, I would not post any of this data because that would be aganist FERPA. The most challenges would be getting the data from the students.

In a professional setting, it would be a lot more harder because I would have to communicate with Industries and STEM teachers to ensure that I can produce the data.]

Question 3: How could these techniques evolve in your field? What would be possible if these tools were integrated into an LMS dashboard that teachers or designers could access in real time?

  • [Heatmap is something we already have access to in a way through using excel sheets, but just being able to have one simple line of code and then a chart would be a lot more easier to develop than an excel sheet. Word cloud and SNA plot would make PDs more interesting to see in real time what data is being collected. All these techniques are already in the field in a different way, but them being all underneath one platform would be better.]

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. This file uses several packages (tm, wordcloud, igraph) that produce warnings during preprocessing — that is expected. As long as the final HTML page appears, the render was successful. If you see a true error (red text that stops the render), check that all packages are installed:

install.packages(c("tm", "wordcloud", "RColorBrewer", "reshape2", "igraph"))

Step 3 — Publish

Option Best for Link
Posit Cloud Quickest — one click from your workspace Guide
RPubs Free, public, easy to share a link rpubs.com
Quarto Pub Clean public portfolio pages Guide
GitHub Pages Best for a professional portfolio Guide
TipE-portfolio tip

This is the most visually impressive of the four files — word clouds, heatmaps, and network graphs are immediately recognizable as advanced data work to anyone reviewing your portfolio. If you are sharing one document from this course with a hiring committee or graduate school application, this is the one to lead with. Pair it with your capstone analysis for the full picture.

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.