Chapter. 1. Capturing the Wealth and Diversity of Learning Processes with Learning Analytics Methods -- Part. I. Getting started -- Chapter. 2. A Broad Collection of Datasets for Educational Research Training and Application -- Chapter. 3. Getting started with R for Education Research -- Chapter. 4. An R Approach to Data Cleaning and Wrangling for Education -- Chapter. 5. Introductory Statistics with R for Educational Researchers -- Chapter. 6. Visualizing and Reporting Educational Data with R -- Part. II. Machine Learning -- Chapter. 7. Predictive Modelling in Learning Analytics using R -- Chapter. 8. Dissimilarity-based Cluster Analysis of Educational Data: A Comparative Tutorial using R -- Chapter. 9. An Introduction and R Tutorial to Model-based Clustering in Education via Latent Profile Analysis -- Part. III. Temporal methods -- Chapter. 10. Sequence Analysis in Education: Principles, Technique, and Tutorial with R -- Chapter. 11. Modeling the Dynamics of Longitudinal Processes in Education. A tutorial with R for The VaSSTra Method -- Chapter. 12. A Modern Approach to Transition Analysis and Process Mining with Markov Models in Education -- Chapter. 13. Multichannel Sequence Analysis in Educational Research Using R -- Chapter. 14. The Why, the How, and the When of Educational Process Mining in R -- Part. IV. Network analysis -- Chapter. 15. Social Network Analysis: A Primer, a Guide and a Tutorial in R -- Chapter. 16. Community Detection in Learning Networks Using R -- Chapter. 17. Temporal Network Analysis: Introduction, Methods, and Analysis with R -- Chapter. 18. Epistemic Network Analysis and Ordered Network Analysis in Learning Analytics -- Part. V. Psychometrics -- Chapter. 19. Psychological Networks: A Modern Approach to Analysis of Learning and Complex Learning Processes -- Chapter. 20. Factor Analysis in Education Research using R -- Chapter. 21. Structural Equation Modeling with R for Education Scientists -- Chapter. 22. Why educational research needs a complex system revolution that embraces individual differences, heterogeneity, and uncertainty.-.

Open Access

This open access comprehensive methodological book offers a much-needed answer to the lack of resources and methodological guidance in learning analytics, which has been a problem ever since the field started. The book covers all important quantitative topics in education at large as well as the latest in learning analytics and education data mining. The book also goes deeper into advanced methods that are at the forefront of novel methodological innovations. Authors of the book include world-renowned learning analytics researchers, R package developers, and methodological experts from diverse fields offering an unprecedented interdisciplinary reference on novel topics that is hard to find elsewhere. The book starts with the basics of R as a programming language, the basics of data cleaning, data manipulation, statistics, and analytics. In doing so, the book is suitable for newcomers as they can find an easy entry to the field, as well as being comprehensive of all the major methodologies. For every method, the corresponding chapter starts with the basics, explains the main concepts, and reviews examples from the literature. Every chapter has a detailed explanation of the essential techniques and basic functions combined with code and a full tutorial of the analysis with open-access real-life data. A total of 22 chapters are included in the book covering a wide range of methods such as predictive learning analytics, network analysis, temporal networks, epistemic networks, sequence analysis, process mining, factor analysis, structural topic modeling, clustering, longitudinal analysis, and Markov models. What is really unique about the book is that researchers can perform the most advanced analysis with the included code using the step-by-step tutorial and the included data without the need for any extra resources. This is an open access book.

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