Affective computing
Machine Learning
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Learning empathy: a data-driven framework for modeling empathetic companion agents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Probabilistic goal recognition in interactive narrative environments
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Corpus-based, statistical goal recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Data-Driven refinement of a probabilistic model of user affect
UM'05 Proceedings of the 10th international conference on User Modeling
Diagnosing self-efficacy in intelligent tutoring systems: an empirical study
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Narrative-Centered tutorial planning for inquiry-based learning environments
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A domain-independent framework for modeling emotion
Cognitive Systems Research
Affective Transitions in Narrative-Centered Learning Environments
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Modeling User Affect from Causes and Effects
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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
Towards Systems That Care: A Conceptual Framework based on Motivation, Metacognition and Affect
International Journal of Artificial Intelligence in Education
Modeling confusion: facial expression, task, and discourse in task-oriented tutorial dialogue
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Generic physiological features as predictors of player experience
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Modeling learner affect with theoretically grounded dynamic bayesian networks
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Affective support in narrative-centered learning environments
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Generalizing models of student affect in game-based learning environments
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Developing empirically based student personality profiles for affective feedback models
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Implicit strategies for intelligent tutoring systems
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Exploiting sentiment analysis to track emotions in students' learning diaries
Proceedings of the 13th Koli Calling International Conference on Computing Education Research
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Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Being able to detect negative affective states early, i.e., before they lead students to abandon learning tasks, could permit intelligent tutoring systems sufficient time to adequately prepare for, plan, and enact affective tutorial support strategies. A first step toward this objective is to develop predictive models of student frustration. This paper describes an inductive approach to student frustration detection and reports on an experiment whose results suggest that frustration models can make predictions early and accurately.