Evaluating Learner Experience from Affective Cues
نویسنده
چکیده
Learning with computers is typically self-paced. Appraisal of the learner’s experience is therefore crucial for making machine-learner interactions ‘truly intelligent’ and instinctive. As a significant aspect of Affective Learning, affect recognition needs to be considered in light of its implications and appliance to computer based learning environments. Drawing on this motivation, I propose to investigate the potential of using facial expression analysis to model affect in learning as a means for evaluating learner state.
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