Ulteriori informazioni
This is an open access book.
This comprehensive and timely methodological book introduces several novel topics under the overarching sections of advanced learning analytics (LA), artificial intelligence (AI), precision education, and complex systems. These topics are presented using accessible language, beginning with introductory chapters that cover the fundamentals of each section, followed by step-by-step tutorials featuring code and datasets for various methods within each area. Although the title refers to advanced LA, the book is written for the broader educational research community and is of interest to quantitative researchers from diverse backgrounds. The first section focuses on Explainable AI and machine learning (ML), with an introduction to the methods, their applications, and tutorials. The second section outlines the foundational concepts of LLMs, their potential applications, and related methodologies, with a tutorial on using LLMs in various analytical tasks. The third section focuses on complex systems, which have become integral to many disciplines and have enabled breakthroughs in modeling intractable problems. Here, three chapters cover Transition Network Analysis (TNA), which fills a critical gap in modeling the temporal unfolding of learning processes over time from a complex systems perspective. The final section addresses precision education, with a particular emphasis on person-centered and person-specific (idiographic) methodologies.
Sommario
1. Introduction.- Section I. Complex systems in education.- 2. Basics of complex systems.- 3. Advanced Applications of Psychological Network.- 4. Complex networks.- 5. Dynamics of Complex systems.- Section II. Advanced predictive analytics and explainable AI.- 6. Introduction to advanced predictive analytics and explainable AI.- 7. Predictive analytics with explainable AI.- 8. Individualized Instance level explainable AI for educational data.- 9. Automatic explainable machine learning for education applications.- 10. A tutorial on penalized regression methods to Identify key factors relevant to students' learning performance.- 11. Advanced Clustering with explanatory covariates.- 12. An introduction to person-specific methods and precision education.- 13. Idiographic Single Subject Explainable Artificial Intelligence.- 14. Individualized analytics for the learning process.- 15. The Application of NLP to Learning Analytics.
Info autore
Mohammed Saqr is an Associate Professor of Computer Science at the University of Eastern Finland (UEF). He holds a PhD in learning analytics from Stockholm University, Sweden. Before joining UEF, he completed a postdoctoral fellowship at Université Paris Cité, France, and obtained the title of Docent in learning analytics from the University of Oulu, Finland. Mohammed established and currently leads UEF’s Learning Analytics (LA) Unit, recognized as Europe’s most productive LA laboratory in the past five years, with a well-established global standing in methodological diversity, innovation, and scientific impact. Mohammed has authored more than 200 peer-reviewed methodological and empirical studies spanning LA, AI, big data and network science.
Riassunto
This is an open access book.
This comprehensive and timely methodological book introduces several novel topics under the overarching sections of advanced learning analytics (LA), artificial intelligence (AI), precision education, and complex systems. These topics are presented using accessible language, beginning with introductory chapters that cover the fundamentals of each section, followed by step-by-step tutorials featuring code and datasets for various methods within each area. Although the title refers to “advanced LA,” the book is written for the broader educational research community and is of interest to quantitative researchers from diverse backgrounds. The first section focuses on Explainable AI and machine learning (ML), with an introduction to the methods, their applications, and tutorials. The second section outlines the foundational concepts of LLMs, their potential applications, and related methodologies, with a tutorial on using LLMs in various analytical tasks. The third section focuses on complex systems, which have become integral to many disciplines and have enabled breakthroughs in modeling intractable problems. Here, three chapters cover Transition Network Analysis (TNA), which fills a critical gap in modeling the temporal unfolding of learning processes over time from a complex systems perspective. The final section addresses precision education, with a particular emphasis on person-centered and person-specific (idiographic) methodologies.
