Managing Learner’s Affective States in Intelligent Tutoring Systems

Abstract. Recent works in Computer Science, Neurosciences, Education, and Psychology have shown that emotions play an important role in learning. Learner’s cognitive ability depends on his emotions. We will point out the role of emotions in learning, distinguishing the different types and models of emotions which have been considered until now. We will address an important issue con-cerning the different means to detect emotions and introduce recent approaches to measure brain activity using Electroencephalograms (EEG). Knowing the influ-ence of emotional events on learning it becomes important to induce specific emo-tions so that the learner can be in a more adequate state for better learning or memorization. To this end, we will introduce the main components of an emotion-ally intelligent tutoring system able to recognize, interpret and influence learner’s emotions. We will talk about specific virtual agents that can influence learner’s emotions to motivate and encourage him and involve a more cooperative work, particularly in narrative learning environments. Pushing further this paradigm, we will present the advantages and perspectives of subliminal learning which inter

Enhancing Learning with Off-Task Social Dialogues

Abstract. In Peoplia, a socially intelligent tutoring agent helps students learn by augmenting learning opportunities with social features. The tutoring agent engages in off-task conversations with the students before and after the instructional activities, motivating them to work with the system more successfully. We describe the tutor's architecture and early experiments in the domain of middle school mathematics. Students who engaged with the socially intelligent agent liked the system more, and attained higher learning gains

Multimodal Analysis of Expressive Gesture in Music Performance

This chapter focuses on systems and interfaces for multimodal analysis of expressive gesture as a key element of music performance. Research on expressive gesture became particularly relevant in recent years. Psychological studies have been a fundamental source for automatic analysis of expressive gesture since their contribution in identifying the most significant features to be analysed. A further relevant source has been research in the humanistic tradition, in particular choreography. As a major example, in his Theory of Effort, choreographer Rudolf Laban describes the most significant qualities of movement. Starting from these sources, several models, systems, and techniques for analysis of expressive gesture were developed. This chapter will present an overview of methods for the analysis, modelling, and understanding of expressive gesture in musical performance. Techniques will be introduced starting from the research developed along years at the Casa Paganini \u2013 InfoMus Research Centre: from early experiments of human-robot interaction in the context of music performance up to recent set-ups of innovative interfaces and systems for active experience of sound and music content. The chapter ends with an overview of possible future research challenges