Learning Analytics Methods and Tutorials

This open-access book provides a comprehensive methodological guide to learning analytics, addressing a long-standing gap in resources and practical guidance for researchers and practitioners. Developed in collaboration with world-renowned learning analytics researchers, R package developers, and methodological experts from diverse fields, it offers an interdisciplinary reference that spans both foundational techniques and cutting-edge innovations. The book begins with the essentials — R programming, data cleaning, manipulation, and statistics — making it accessible to newcomers. It then progressively advances to sophisticated methods at the forefront of the field: predictive modeling, clustering, sequence analysis, process mining, social and temporal network analysis, epistemic networks, psychological networks, factor analysis, and structural equation modeling. Every chapter pairs methodological explanation with step-by-step R tutorials using real, open-access educational datasets, ensuring that readers can immediately apply what they learn. What makes this volume distinctive is its breadth and coherence. Rather than treating each method in isolation, the chapters are interconnected, showing how different analytical lenses — from network structures to temporal sequences to latent variable models — can be combined to build a richer understanding of learning processes. The result is a practical guide that serves equally well as a textbook for graduate courses in learning analytics and as a hands-on reference for active researchers.

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