Computational Methods

Overview

This section introduces the core computational methods that form the basis of educational data analysis. Across the next three chapters, you will learn how to analyze textual, relational, and numeric data—three major forms of information that appear in learning environments, institutional records, and educational research projects.

These chapters emphasize hands-on, transparent, and reproducible approaches using R. By engaging with real examples, you will gain practical experience in transforming raw educational data into interpretable results that inform theory and practice.

The Analytical Scope of This Section

Chapter Data Type Analytical Focus Example Research Question
Chapter 2 Text Data (unstructured) Natural language processing, tokenization, sentiment, topic modeling “What themes emerge in student reflections or policy statements?”
Chapter 3 Relational Data Social network analysis: centrality, community detection, visualization “How do students or instructors connect and collaborate in learning networks?”
Chapter 4 Numeric / Big Data Statistical modeling, regression, clustering, predictive analysis “Which factors best predict academic outcomes or engagement?”

📚 These three perspectives together illustrate how computational techniques can capture different dimensions of learning—language, interaction, and measurement.

Setting Up the Computational Environment

Before exploring the examples, ensure that your R environment contains the essential packages used throughout this section.

install.packages(c(
  # Core workflow and visualization
  "tidyverse", "ggplot2", "readr", "stringr",

  # Text analysis
  "tidytext", "quanteda", "textdata",

  # Network analysis
  "igraph", "ggraph", "tidygraph",

  # Numeric and machine learning tools
  "caret", "cluster"
))

Optional visualization and interaction packages:

install.packages(c("plotly", "RColorBrewer", "visNetwork"))

💡 Tip: Use a consistent project structure so each chapter builds on the same foundation:

project/
├── data/        # datasets
├── scripts/     # reusable code
├── outputs/     # tables and processed files
└── figures/     # charts and visualizations

This structure promotes reproducibility and helps keep analysis pipelines organized.

A General Computational Workflow

Regardless of data type, the analytical logic follows a similar cycle:

  1. Load data — read files from local or online sources.
  2. Clean data — handle missing values, normalize text or numeric fields.
  3. Transform data — create tokens, build networks, or scale variables.
  4. Analyze — apply the method appropriate to the data form.
  5. Visualize and interpret — generate plots and summaries to support interpretation.
library(tidyverse)

data <- read_csv("data/example.csv")

cleaned <- data |>
  mutate(across(everything(), str_squish)) |>
  drop_na()

summary(cleaned)

🧩 This five-step workflow—load, clean, transform, analyze, interpret—appears throughout all chapters in this section.

Ethics, Transparency, and Reproducibility

Educational data often include sensitive or identifiable information.

Responsible computational research requires attention to both ethical and methodological rigor.

  • Privacy: Remove or anonymize all personal identifiers.
  • Transparency: Keep analysis scripts in Quarto or R Markdown files for version control.
  • Reproducibility: Record package versions and parameters used in each analysis.
  • Interpretability: Combine quantitative patterns with contextual educational insight.

⚖️ Ethical and transparent practices ensure that computational results remain credible, interpretable, and usable for improving learning.

Transition to Analytical Chapters

With the environment prepared and workflow established, you are ready to begin applying computational methods to real educational data:

  • Chapter 2 — Text Analysis: Working with unstructured language to uncover patterns in meaning and discourse.
  • Chapter 3 — Network Analysis: Examining connections and relationships among learners, instructors, or resources.
  • Chapter 4 — Numeric and Big Data: Exploring structured data to identify trends and predictors in education.

The following chapter begins with text data, illustrating how natural language processing can transform qualitative information into structured, interpretable results.