The last years we are working with serveral data sets that contain emotion measurements on different levels (self-report, facial action coding, eye tracking, physiological data like heart rate and skin conductance, lexical and sentiment analysis of texts).
We do not naively believe that the components of emotions match all the time or over longer or shorter time spans. But stil we search for correspondences between different emotional components.
Our aim is to measure and to integrate emotions in technology based learning designs.
Are there new ideas how to bring different components of emotions together?
What's about micro emotions, peak values, long or short term patterns; how can they help to identify correspondences?
Hi. I am also interested in this type of research. I have looked at it from a slight different perspective: the type of communication channel, visual, additive or tactile. When content is augmented with visual-haptic interfaces learners seem to connect more not only with content but also among themselves. Some work related to abstract/complex concept delivery using visuo-haptic interfaces attached. Let me know if you are interested to collaborate on this.
One reason that polygraph information is not admissible is the ability of a certain percentage of people to control physiological responses.
In classes, traditional "theatrical methods" are used to gain attention (recordings of accidents, dramatizations, first person accounts) to emphasize important points. Evoking emotions apparently increases training retention up to a point (overload is possible). However, the only measurement we obtain is student feedback. Self-report does vary widely.
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