Primary workspace
See the workflow in Orange
Orange Data Mining makes each analytical step visible, so you can focus on research choices before worrying about syntax.
Korean Studies · Digital Humanities
BA2 Digital Korea
Learn to turn Korean-language texts into evidence. Across twelve hands-on sessions, you will build a transparent research workflow with Orange Data Mining, develop a foundation in R, and learn where computational methods illuminate texts—and where they do not.
- Dates
- Feb. 2–May 11, 2026
- Class time
- Mondays, 15:15–17:00
- Locations
- Huizinga 0.09 · Arsenaal B0.05
Course map
Twelve steps from text to research
The sequence moves from responsible data preparation to interpretation. Each week adds one method while keeping the source texts and research question in view.
Week 1Feb. 02
Orientation
Introduction & Getting Started
Frame a feasible text-as-data question and assemble the tools you will use throughout the course.
View course materials →Week 2Feb. 09
Foundations
Computational Text Analysis
Connect research questions, corpora, representations, and analytical choices in one transparent workflow.
View course materials →Week 3Feb. 16
Preparation
Text Preprocessing Basics
Tokenize, normalize, and filter text while documenting what each decision removes or preserves.
Open interactive exercise →Week 4Feb. 23
Representation
From Words to Numbers
Use bag-of-words counts and TF-IDF to compare vocabulary and make descriptive patterns visible.
Explore the materials →Week 5Mar. 02
Practice
Hands-on Methods Lab
Consolidate the first half of the workflow through guided analysis, troubleshooting, and reflection.
Review assignments →Week 6Mar. 09
Assessment
Midterm Review
Demonstrate your command of the workflow and explain the reasoning behind key analytical decisions.
Open midterm guide →Week 7Mar. 16
Similarity
Clustering
Group documents by vocabulary and test whether the resulting clusters make substantive sense.
Open interactive exercise →Week 8Mar. 30
Meaning
Word Embeddings
Explore how context-based representations capture relationships among words across a corpus.
Open embeddings explorer →Week 9Apr. 13
Classification
Sentiment Analysis
Apply dictionary-based scoring and inspect the linguistic cases where simple labels break down.
Open sentiment explorer →Week 10Apr. 20
Discovery
Topic Modeling
Fit and interpret a topic model, then check statistical themes against the documents that produced them.
Open topic explorer →Week 11May 04
Assessment
Research Methods Project
Bring the full pipeline together in a documented analysis of Korean-language primary sources.
Open assessment guide →Week 12May 11
Synthesis
Final Paper Workshop
Turn an analytical result into a clear argument with appropriate evidence, limitations, and documentation.
Open final paper guide →How the learning cycle works
- Begin with a Korean Studies question and identify the texts that could answer it.
- Learn the analytical idea and the assumptions behind it.
- Build the workflow in Orange, then reinforce core concepts through guided R practice.
- Inspect the output against real source texts instead of treating a model as the answer.
- Save, document, and share the work so another person can understand what you did.
Assessment
Three connected components
Practice, method, and interpretation build toward one another.
15%Participation & skills work
35%Research Methods Project
50%Final paper
Programming foundation
Build fluency in R
Short R tutorials help you understand the structure behind the workflow and prepare for more advanced methods courses.