In this cutting-edge book, Andy Nguyen, Kshitij Sharma and Ha Nguyen explain how AI can improve our understanding of how people learn. The authors demonstrate how, by analysing multimodal data from different channels, including eye gazes, physiological data and self-reports, AI can provide a clearer picture of what learners think, feel, and do, going far beyond traditional tests or surveys.
The authors introduce both classical and deep learning methods for collecting and analyzing data, with a particular focus on multimodal learning process analytics. They explore how large language models and generative AI tools can facilitate human-AI collaboration, while addressing ethical and privacy issues such as fairness, transparency and inclusivity. Integrating theoretical, methodological, and practical perspectives, authors demonstrate how using AI responsibly and thoughtfully can create more inclusive, adaptive and evidence-based learning environments.
Artificial Intelligence in Multimodal Learning Process Analytics is a vital reference for scholars and students of learning sciences and pedagogy, particularly those focusing on educational technology, learning analytics and human-computer interaction. It is also highly beneficial for educators and educational policymakers interested in how AI can support learning.
Arvustused
Andy Nguyen, Kshitij Sharma and Ha Nguyen have written a clear, thoughtful, and practical introduction to a cutting-edge direction in education research. Multimodal learning processing analytics holds tremendous potential to revolutionize how we understand and support learning, and this book can turn the curious newcomer into a knowledgeable explorer of these techniques. -- Jeffrey A. Greene, University of North Carolina at Chapel Hill, USA
Contents
Preface
PART I INTRODUCTION TO MULTIMODAL LEARNING
PROCESS ANALYTICS
1 Foundations of multimodal learning process analytics
2 Theoretical frameworks in multimodal learning process
analytics
3 Ethical considerations for AI in multimodal learning process
analytics
PART II AI-ENHANCED MULTIMODAL DATA
COLLECTION AND PROCESSING FOR
CAPTURING LEARNING PROCESSES
4 Collecting multimodal data to capture learning processes
5 Preprocessing multimodal learning process data
PART III AI METHODS FOR ANALYZING MULTIMODAL
DATA ON LEARNING PROCESSES
6 Classical machine learning modeling
7 Deep learning modelling
8 Generative artificial intelligence methods for multimodal
learning process analytics
PART IV FUTURE DIRECTIONS AND FINAL REMARKS
9 Future directions for multimodal learning process analytics
10 Conclusion and final remarks
Bibliography
Andy Nguyen, Assistant Professor, Learning and Educational Technology (LET) Research Lab, University of Oulu, Finland, Kshitij Sharma, Associate Professor, Norwegian University of Science and Technology, Norway and Ha Nguyen, Assistant Professor, Learning Sciences and Psychological Studies, University of North Carolina at Chapel Hill, USA
