About the book
Computational Analysis of Educational Data: A Field Guide Using R is a practical guide for educational researchers who want to use computational methods responsibly and effectively. The book connects research questions, data preparation, analysis, interpretation, and communication through reproducible examples in R.
Rather than treating computational techniques as isolated tools, the book shows how they can support complete educational research workflows. Chapters pair methodological explanations with focused examples that help readers move from a substantive question to an analysis and a clear account of results.
What the book covers
Text data
Capturing, preparing, and analyzing text with computational methods suited to educational research.
Relational data
Using social network analysis to study relationships, interactions, and structures in educational settings.
Large-scale numeric data
Conducting secondary analyses of large educational datasets with transparent and reproducible workflows.
AI and LLMs
Working with cloud-based and local language models while attending to privacy, interpretation, and research design.
Multimodal data
Analyzing images, video, and audio with local AI models and computational research tools.
Communication and collaboration
Developing reproducible practices for documenting, sharing, and communicating computational research.
My role
I am the first author of the book. I am leading the development of its overall structure and contributing to the integration of educational research questions, computational workflows, R examples, and emerging AI methods into a coherent field guide.
The book is co-authored with Mete Akcaoglu, Joshua Rosenberg, and Shaun Kellogg.
Intended audience
The field guide is designed for:
- Educational researchers beginning to use computational methods.
- Graduate students in educational technology, learning sciences, and education data science.
- Researchers seeking practical examples of text, network, large-scale, and multimodal analysis.
- Teams developing transparent and reproducible workflows involving R and modern AI tools.
Publication status
- Publisher: CRC Press
- Series: Chapman & Hall/CRC Big Data Series
- Status: Under contract
- Expected publication: Late 2026
- Online companion: Planned for release in late 2026
- Authors: Wei Wang, Mete Akcaoglu, Joshua Rosenberg, and Shaun Kellogg
The project website currently serves as the development home for the book. Publication details and final materials will be updated as the CRC Press edition and online companion approach release.