Titlebook: Affective Computing and Intelligent Interaction; Fourth International Sidney D’Mello,Arthur Graesser,Jean-Claude Martin Conference proceedi

Infirm

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978-3-642-24599-2Springer-Verlag GmbH Berlin Heidelberg 2011

guardianship

Begriffliche und konzeptionelle Grundlagen,of humans and machines via the development of affective and multimodal intelligent systems. This development appears . based on the popular notion that emotions play an important part in social interactions. In fact, research shows that humans missing the possibility to express or perceive/interpret

Dawdle

https://doi.org/10.1007/978-3-8349-8414-2search on interactive learning environments. The intelligent tutoring systems community has begun actively exploring computational models of affect, and game-based learning environments present a significant opportunity for investigating student affect in interactive learning. One family of game-bas

amenity

Begriffliche und konzeptionelle Grundlagen, you are on the go, e.g. the Affectiva Q. Sensor for capturing sympathetic nervous system activation, or without distracting you while you are online, e.g. webcam-based software capturing heart rate variability and facial expressions. We are also developing new technologies that capture and respond

puzzle

Begriffliche und konzeptionelle Grundlagen,commonly used to detect affective states from physiological data. Previous studies have achieved some success in detecting affect from physiological measures, especially in controlled environments where emotions are experimentally induced. One challenge that arises is that physiological measures are

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https://doi.org/10.1007/978-3-8349-8414-2thin two versions of a virtual laboratory for chemistry. This analysis is conducted using field observation data collected within undergraduate classes using the virtual laboratory software as part of their regular chemistry classes. We find that off-task behavior co-occurs with boredom, but appears

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magnanimity

Begriffliche und konzeptionelle Grundlagen,turalistic recordings of scenario meetings. The core method consists of computing a set of prosody and voice quality measures, followed by a Principal Components Analysis (PCA) and Support Vector Machine (SVM) classification to identify the core factors predicting the associated social signal or rel

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