Modeling parallel and reactive empathy in virtual agents: an inductive approach
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
A Look at the Roles of Look & Roles in Embodied Pedagogical Agents - A User Preference Perspective
International Journal of Artificial Intelligence in Education
Affective game engines: motivation and requirements
Proceedings of the 4th International Conference on Foundations of Digital Games
Responding to Learners' Cognitive-Affective States with Supportive and Shakeup Dialogues
Proceedings of the 13th International Conference on Human-Computer Interaction. Part III: Ubiquitous and Intelligent Interaction
Sensors Model Student Self Concept in the Classroom
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Modelling affect expression and recognition in an interactive learning environment
International Journal of Learning Technology
Cohesion Relationships in Tutorial Dialogue as Predictors of Affective States
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
The Illusion of Adaptivity as Instructional Method in Learning Environments
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
User Modeling and User-Adapted Interaction
Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
Computer Speech and Language
Using affective parameters in a content-based recommender system for images
User Modeling and User-Adapted Interaction
PlayPhysics: an emotional games learning environment for teaching physics
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Evaluating the effect of gesture and language on personality perception in conversational agents
IVA'10 Proceedings of the 10th international conference on Intelligent virtual agents
Modeling users of crisis training environments by integrating psychological and physiological data
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Does self-efficacy matter when generating feedback?
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Encouraging students to study more: adapting feedback to personality and affective state
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Mining multimodal sequential patterns: a case study on affect detection
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Controlling user perceptions of linguistic style: Trainable generation of personality traits
Computational Linguistics
Subliminally enhancing self-esteem: impact on learner performance and affective state
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Social and caring tutors: ITS 2010 keynote addres
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
What do children favor as embodied pedagogical agents?
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
Integrating learning, problem solving, and engagement in narrative-centered learning environments
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Tractable POMDP representations for intelligent tutoring systems
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Proceedings of the 17th Panhellenic Conference on Informatics
Using Emotional Intelligence in Training Crisis Managers: The Pandora Approach
International Journal of Distance Education Technologies
Hi-index | 0.00 |
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 student's level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.