A Computational Model for Managing Emotions and Affections in Emotional Learning Platforms and Learning Experience in Emotional Computing Context

Abstract

In this paper we propose a model to represent and manage affective / emotional feedback. This model represents a set of emotions which is assumed to play a major role for learning processes. The emotions are to be identified during the learning phase. To achieve these objectives, we have considered primarily: i) Methods and techniques to assess and identify affectivity/ emotivity in ITS (Intelligent Tutoring System), ii) Feedback management derived by the detection of user's affective states in ITS. Our objective is to establish a methodology through the steps that lead us to 'identification and quantification of the emotional state of a learner. © 2012 Crown Copyright

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“…Six basic, universally recognized emotions were defined in 1972 by Ekman, Friesen, and Ellsworth: anger, disgust, fear, joy, sadness, and surprise. This list has been expanded by other researchers (e.g., Calvo & D'Mello, 2010;Ekman & Davison, 1994;Iovane et al, 2012). Methods and techniques for detecting affectivity have been proposed on the basis of these definitions (Luneski et al, 2010).…”

“…Many studies have been carried out to describe how specific body features may be used to recognize specific affective states (Kleinsmith & Bianchi-Berthouze, 2013;Tan, Schöning, Luyten, & Coninx, 2013). Some researchers have focused on assessing only a reduced set of basic emotions, in a manner similar to that used in the facial expression technique, in order to simplify the posture-based affect detection process (Calvo & D'Mello, 2010;Iovane et al, 2012;Luneski et al, 2010). Head movement as a postural response also has been used to estimate attentional mechanisms when monitoring users' cognitive engagement (Dirican & Göktürk, 2012).…”

“…Methods that use physiological data or signal recognition usually are included within what is referred to as machine learning techniques (Calvo & D'Mello, 2010;Luneski, Bamidis, & Hitoglou-Antoniadou, 2008;Luneski et al, 2010). Several noninvasive measurements can be acquired by recording electrical signals produced by the brain, heart, muscles, and skin (Iovane et al, 2012). These measurements can be obtained nowadays through unobtrusive wearable sensing devices or through devices embedded in the surrounding environment.…”

“…For example, anger can be expressed by facial, vocal, body posture, and physiological changes. Based on these premises, some studies have proposed a technique called multimodality, which integrates information obtained from several sources and methods (Calvo & D'Mello, 2010;Iovane et al, 2012). In a recent study, Iovane et al (2012) proposed the use of data fusion, feature fusion, and decision fusion (see Table 1) to combine or "fuse" signals from several sensors.…”

The effects of biofeedback‐based stimulated recall on self‐regulated online learning: A gender and cognitive taxonomy perspective

“…Stimulated recall comprises introspective procedures through which participants' cognitive processes help learners engage in more effective learning by adopting a stimulus (normally a recorded video) to be delivered to the student at the time of learning (Iovane, Salerno, Giordano, Ingenito, & Mangione, ). Bloom () observed that SR could be useful for examining humans' covert cognitive behavior.…”