Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
A domain-independent framework for modeling emotion
Cognitive Systems Research
Early Prediction of Student Frustration
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Viewing Student Affect and Learning through Classroom Observation and Physical Sensors
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Coping with Frustration: Self-efficacy Modelling and Empathetic Companion Agents
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Recognizing and Responding to Student Affect
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Affect-aware tutors: recognising and responding to student affect
International Journal of Learning Technology
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
“Yes!”: using tutor and sensor data to predict moments of delight during instructional activities
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Social and caring tutors: ITS 2010 keynote addres
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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Self-efficacy is an individual's belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a students' level of self-efficacy. This paper investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. In an empirical study, two families of self-efficacy models were induced: a static model, learned solely from pre-test (non-intrusively collected) data, and a dynamic model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. The resulting static model is able to predict students' real-time levels of self-efficacy with reasonable accuracy, while the physiologically informed dynamic model is even more accurate.