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Dynamics of affective states during complex learning
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Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
TLDR
This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations.
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Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments
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Confusion can be beneficial for learning.
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Toward an Affect-Sensitive AutoTutor
TLDR
Here, the possibility of enabling AutoTutor, an intelligent tutoring system, to process learners' affective and cognitive states is considered.
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Boring but important: a self-transcendent purpose for learning fosters academic self-regulation.
TLDR
This research proposed that promoting a prosocial, self-transcendent purpose could improve academic self-regulation on such tasks and found that those with more of a purpose for learning persisted longer on a boring task rather than giving in to a tempting alternative and were less likely to drop out of college.
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Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features
TLDR
A multimodal affect detector that combines conversational cues, gross body language, and facial features, and linear discriminant analyses to discriminate between naturally occurring experiences of boredom, engagement/flow, confusion, frustration, delight, and neutral is developed and evaluated.
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Automatic detection of learner’s affect from conversational cues
We explored the reliability of detecting a learner’s affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system (ITS) that helps students learn by…
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A Review and Meta-Analysis of Multimodal Affect Detection Systems
TLDR
A quantitative review and meta-analysis of 90 Multimodal affect detection systems revealed that MM systems were consistently (85% of systems) more accurate than their best unimodal counterparts, with an average improvement of 9.83% (median of 6.60%).
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AutoTutor and affective autotutor: Learning by talking with cognitively and emotionally intelligent computers that talk back
TLDR
AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by interacting with them in natural language with adaptive dialog moves similar to those of human tutors.
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