The overall goal of our research is to develop a system of intelligent multimodal affective pedagogical agents that are effective for different types of learners (Adamo et al., 2021). While most of the research on pedagogical agents tends to focus on the cognitive aspects of online learning and instruction, this project explores the less-studied role of affective (or emotional) factors. We aim to design believable animated agents that can convey realistic, natural emotions through speech, facial expressions, and body gestures and that can react to the students’ detected emotional states with emotional intelligence. Within the context of this goal, the specific objective of the work reported in the paper was to examine the extent to which the agents’ facial micro-expressions affect students’ perception of the agents’ emotions and their naturalness. Micro-expressions are very brief facial expressions that occur when a person either deliberately or unconsciously conceals an emotion being felt (Ekman &Friesen, 1969). Our assumption is that if the animated agents display facial micro expressions in addition to macro expressions, they will convey higher expressive richness and naturalness to the viewer, as “the agents can possess two emotional streams, one based on interaction with the viewer and the other based more »
- Award ID(s):
- 1821894
- Publication Date:
- NSF-PAR ID:
- 10340819
- Journal Name:
- AHFE International
- Volume:
- 22
- ISSN:
- 2771-0718
- Sponsoring Org:
- National Science Foundation
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The goal of this research is to develop Animated Pedagogical Agents (APA) that can convey clearly perceivable emotions through speech, facial expressions and body gestures. In particular, the two studies reported in the paper investigated the extent to which modifications to the range of movement of 3 beat gestures, e.g., both arms synchronous outward gesture, both arms synchronous forward gesture, and upper body lean, and the agent‘s gender have significant effects on viewer’s perception of the agent’s emotion in terms of valence and arousal. For each gesture the range of movement was varied at 2 discrete levels. The stimuli of the studies were two sets of 12-s animation clips generated using fractional factorial designs; in each clip an animated agent who speaks and gestures, gives a lecture segment on binomial probability. 50% of the clips featured a female agent and 50% of the clips featured a male agent. In the first study, which used a within-subject design and metric conjoint analysis, 120 subjects were asked to watch 8 stimuli clips and rank them according to perceived valence and arousal (from highest to lowest). In the second study, which used a between-subject design, 300 participants were assigned to two groups ofmore »
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We introduce a novel framework for emotional state detection from facial expression targeted to learning environments. Our framework is based on a convolutional deep neural network that classifies people’s emotions that are captured through a web-cam. For our classification outcome we adopt Russel’s model of core affect in which any particular emotion can be placed in one of four quadrants: pleasant-active, pleasant-inactive, unpleasant-active, and unpleasant-inactive. We gathered data from various datasets that were normalized and used to train the deep learning model. We use the fully-connected layers of the VGG_S network which was trained on human facial expressions that were manually labeled. We have tested our application by splitting the data into 80:20 and re-training the model. The overall test accuracy of all detected emotions was 66%. We have a working application that is capable of reporting the user emotional state at about five frames per second on a standard laptop computer with a web-cam. The emotional state detector will be integrated into an affective pedagogical agent system where it will serve as a feedback to an intelligent animated educational tutor.
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