Lesson 1 - Foundations of affective computing for learning

SUMMARY

Learning is never purely rational. How learners feel plays a crucial role in how they engage, persist, and acquire new skills. This first lesson builds the foundation for understanding why that is the case.

Have you ever wondered why two learners with the same skills can experience the same task so differently, one feeling motivated and absorbed, the other bored or frustrated? In this lesson, you will explore the basic ideas behind affective computing and why emotions, engagement, and learning states matter in education and training contexts.

You will be introduced to key affective concepts such as emotion, mood, personality, and flow, and learn how they differ in duration, influence, and relevance for learning. The lesson also provides a high-level overview of the main affect models used in affective computing, helping you understand how complex human experiences can be represented in a structured way. Through educational examples drawn from digital learning environments and serious games, you will see how these concepts already shape modern learning technologies.

By the end of this lesson, you will have a shared vocabulary and conceptual framework that will support the rest of the course, preparing you to explore how affect can be measured, interpreted, and used to design more effective and human-centred learning systems.

About The Author

Dr Enrique Hortal is an Assistant Professor in the Department of Advanced Computing Sciences at Maastricht University. His academic and research work focuses on affective computing, brain–computer interfaces, and machine learning for the analysis of physiological and brain signals. He brings extensive experience in computational intelligence and human-centred AI, combining theoretical foundations with practical applications in emotion recognition and intelligent systems.