Addressing Emotions within E-Learning Systems
Valentino Zurloni (CESCOM, University of Milan - Bicocca, Italy), Fabrizia Mantovani (CESCOM, University of Milan - Bicocca, Italy and ATN-P LAB, Istituto Auxologico Italiano, Italy), Marcello Mortillaro (CESCOM, University of Milan - Bicocca, Italy and CISA - University of Geneva, Switzerland), Antonietta Vescovo (CESCOM, University of Milan - Bicocca, Italy) and Luigi Anolli (CESCOM, University of Milan - Bicocca, Italy)
Copyright: © 2008
Emotions are attracting growing attention within the instructional design research community. However, clarification is still required as to how exactly to address emotions within the field of e-learning. The aim of this chapter is twofold. Firstly, we will focus on the reasons for including emotions within the instructional technology domain, and in particular, on the relevance of emotions to computer-based learning. The need for specific theory in this regard is heightened by the current drive to design instructional devices that interact with learners in a motivating, engaging, and helpful way. Secondly, within the framework affective computing paradigm, the different modalities for detecting emotions in instructional technology contexts will be systematically reviewed, and the strengths and limits of each will be discussed on the basis of the most up to date research outcomes. Finally, a tentative architecture for emotion recognition in computer-based learning will be proposed, focusing on the adoption of a multimodal approach to emotion recognition, in order to overcome the limitations and the difficulties associated with individual modalities.
Key Terms in this Chapter
Facial Action Coding System (FACS): FACS is a system originally developed by Paul Ekman and Wallace Friesen in 1978 to taxonomize human facial expression. It is the most used method to measure and describe facial behaviors, coded through action units (AU).
Computer Anxiety: Computer anxiety is the individual fear or apprehension of using a computer directly or the anticipation of having to use it.
Emotional Intelligence: Emotional intelligence is the underlying general competence that comprises a variety of emotional skills. Such skills vary according to the different models of emotional intelligence proposed.
Multimodal Emotion Recognition: Multimodal emotion recognition is where one is inferring an emotional state and integrating inputs coming from multiple emotion-sensitive sources.
Appraisal: Appraisal refers to the cognitive evaluation antecedent to an emotional episode. Appraisal theoretical models are characterized and differentiated by the appraisal dimensions included.
Autonomic Nervous System (ANS): ANS is the part of the nervous system that regulates individual organ function and homeostasis, and for the most part, is not subject to voluntary control. It is usually divided into sympathetic and parasympathetic.
Modality (of emotional detection): Modality is each of the different channels that are considered to be emotion-sensitive (i.e., physiological measures, vocal non-verbal measures, self-report measures, facial expressions, posture and gestures, and verbal content).
Affective Computing: Affective computing is a research field that aims at including emotions within information technology design. It deals specifically with three different levels of emotion integration: (1) the detection of user emotions, (2) the expression of emotions by computers, and ultimately, (3) the possibility for a computer to “have” emotions.