Michalis Feidakis

Design of an emotion aware e-learning system

Sense and analysis of users emotions has appeared in the focus of the research agenda during the last decade with many applications, especially in learning. The latter is informed by the study of emotions that has been attained for many years. In the current paper, we first review prominent theories and models of learning with respect to students emotions and emotion recognition technologies and then we analyse the design of a system endowed with emotion awareness features. Part of the work we present, is based on the results of a pilot CSCL project, named emot-project. In our last section, we define an agenda for future work.

Providing emotion awareness and affective feedback to virtualised collaborative learning scenarios

The enrichment of computer supported collaborative learning (CSCL) systems with emotion awareness features (detect emotion patterns and respond affectively) opens a window towards an authentic social interaction and learning experience. In the current paper, we first review prominent emotion theories and models with respect to learning, detection techniques and affective feedback strategies, and then we present a system’s design and implementation that provides collaborators with a usable, web tool for self-reporting of emotions. In response to the user’s emotions, the system activates an animated, virtual assistant that employs expressive faces and synthesised speech to provide affective and task-based feedback. The specific tool was customised and adapted to the virtualised collaborative session (VCS) system for the needs of an experiment that we run in the Open University of Catalonia. The respective results and findings are presented and discussed.

A Multi-fold Time Approach to Address Emotions in Live and Virtualized Collaborative Learning

The enrichment of Computer Supported Collaborative (CSCL) Systems with emotion awareness features (detect emotion patterns and respond effectively) opens a window to the future of learner-to-computer interaction. In the current paper we present a systems design that attempts to evaluate the learners affective state while he/she is taking part into collaborative tasks, in three different time-points: (i) before the task, (ii) in real-time (while collaborating) and (iii) retrospective (after the task). For the emotion assessment process, self-reporting and sentiment analysis are explored. Based on effective production rules that correlate emotions or emotion sequences to affective feedback techniques, the system will provide emotion scaffolds corresponding to user needs and feelings.

A Five-Layer Approach in Collaborative Learning Systems Design with Respect to Emotion

This work presents a position paper that first reviews in depth the state of the art in Collaborative Learning Systems design with respect to emotion and then proposes a new conceptual framework that takes into account a five layer approach that considers physiological, emotional, sociological, technological and educational aspects in relation to learning. This study reveals the need for a more situated and affective Computer Supported Collaborative Learning system.

A Framework for Designing Computer Supported Learning Systems with Sensibility

In the current paper, the authors propose a new conceptual framework in Computer Supported Learning System-CSCL design that takes emotion into account. They conduct their analysis through five layers: physiological, emotional, sociological, technological, and educational, in relation to learning. This study reviews the literature in emotional learning and emotion assessment and proposes a model for the design of a social-emotional computer supported learning system.

Towards a Social-Emotional e-Learning System

In the current paper, we first review Emotion-Learning Theories and Emotion Assessment in Human-Computer Interaction (HCI) and then we lay the foundations of a Social-Emotional Computer Supported Learning System that is based on social-emotional competencies and can support emotion assessment.