Advanced Learning Analytics Methods : AI, Precision and Complexity / edited by Mohammed Saqr, Sonsoles López-Pernas.

Format:

Book

Contributor:

Saqr, Mohammed., Editor.

López-Pernas, Sonsoles., Editor.

Series:

Computer Science Series

Language:

English

Subjects (All):

Education--Data processing.

Education.

Educational technology.

Data mining.

Social sciences--Data processing.

Social sciences.

Computers and Education.

Digital Education and Educational Technology.

Data Mining and Knowledge Discovery.

Computer Application in Social and Behavioral Sciences.

Local Subjects:

Computers and Education.

Digital Education and Educational Technology.

Data Mining and Knowledge Discovery.

Computer Application in Social and Behavioral Sciences.

Physical Description:

1 online resource (XXI, 593 p. 146 illus., 136 illus. in color.)

Edition:

1st ed. 2026.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2026.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9783031953651

OCLC:

1550436805