Emotions and Learning with AutoTutor
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
International Journal of Human-Computer Studies
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
Exploring the relationship between novice programmer confusion and achievement
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Emotion generation integration into cognitive architecture
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
The intricate dance between cognition and emotion during expert tutoring
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
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We explored the complex interplay between students' affective states and problem solving outcomes. We conducted a study where 41 students solved 28 analytical reasoning problems from the Law School Admission Test. Participants viewed videos of their interaction history and judged their emotions at theoretically relevant points in the problem solving session (after new problem is displayed, in the midst of problem solving, after feedback is received). We explore excitatory and inhibitory relationships between the affective states and problem solving outcomes (i.e. success or failure, and associated positive or negative feedback). We isolate affective states that are consequences of outcomes and associated feedback as well as affective states that are antecedents of positive or negative outcomes. Follow-up analyses focused on cyclical patterns that incorporate complex relationships between the affective states and problem solving outcomes. Implications of our results for affect-sensitive artificial learning environments are discussed.