Workshop: Introduction to Computational Studies in Education and the Social Sciences

Author
Affiliations

Nathan Alexander, PhD

School of Education

Center for Applied Data Science and Analytics

Welcome students and colleagues! Thank you for joining us. This site is to support your learning during today’s introduction to Computational Studies in Education and the Social Sciences.

My name is Professor Nathan Alexander and I will be your facilitator today.

Computational Studies in Education (CSIE) is an interdisciplinary field that uses computational methods — like data analysis, modeling, and machine learning — to study how people learn, behave, and interact in social and educational contexts. At its core, it blends ideas from Education, Social Sciences, and Computer Science.

Instead of relying only on traditional paper-based methods that focus on small-scale surveys or interviews, this field works with large-scale and complex data for:

Given our time constraints today, we’ll focus on a few key foundations.

Learning Outcomes

By the end of this workshop, participants will be able to:

  • Perform basic data analysis workflows in R using real-world educational and social science datasets
  • Import, clean, and visualize data using tidyverse tools
  • Produce reproducible reports using R Markdown (Quarto)
  • Formulate and interpret equity-focused research questions using data
  • Extend these methods to support independent research and analysis

Schedule

Part Topic Duration Time
Welcome & site orientation 10 minutes 5:00–5:10 PM
1 Getting started in R and Posit 30 minutes 5:10–5:40 PM
Break 5 minutes 5:40–5:45 PM
2 Conducting a literature scan 15 minutes 5:45–6:00 PM
Break 5 minutes 6:00–6:05 PM
3 Working with large-scale data 45 minutes 6:05–6:50 PM
Q&A 10 minutes 6:50–7:00 PM