Computational Analysis of Educational Data: A Field Guide Using R – Hardcover

Computational Analysis of Educational Data: A Field Guide Using R is a practical guide for conducting computational research in education with transparent, reproducible workflows. It walks readers from setup and core R practices to applied analysis across text, network, and numeric data, with examples designed for direct adaptation in real projects.

The book’s defining feature is its method-to-practice structure: each chapter combines conceptual framing, executable code, and guidance on interpretation and reporting. Readers learn how to build and document end-to-end workflows using Quarto and R, apply common techniques such as sentiment analysis, topic modeling, social network analysis, and predictive modeling, and integrate local or cloud-based large language model tools in a methodologically responsible way. The final chapters focus on communication and dissemination, including manuscript-ready reporting, figure and table conventions, and open-science sharing through platforms such as GitHub Pages, OSF, and Zenodo.

This book is written for educational researchers, learning analytics scholars, doctoral students, and applied research teams who want to strengthen computational capacity without sacrificing research rigor. It is especially useful for readers who already know basic quantitative or qualitative methods and need a clear, reproducible path for implementing contemporary computational approaches in educational contexts.