Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Affective interactions: the computer in the affective loop
Proceedings of the 10th international conference on Intelligent user interfaces
Engagement tracing: using response times to model student disengagement
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Detecting the Learner's Motivational States in An Interactive Learning Environment
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Classifying learner engagement through integration of multiple data sources
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Detecting when students game the system, across tutor subjects and classroom cohorts
UM'05 Proceedings of the 10th international conference on User Modeling
Developing a generalizable detector of when students game the system
User Modeling and User-Adapted Interaction
Temporal Data Mining for Educational Applications
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Asset priority risk assessment using hidden markov models
Proceedings of the 10th ACM conference on SIG-information technology education
Off-Task Behavior in Narrative-Centered Learning Environments
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Discovering Tutorial Dialogue Strategies with Hidden Markov Models
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Ontological technologies for user modelling
International Journal of Metadata, Semantics and Ontologies
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
Characterizing the effectiveness of tutorial dialogue with hidden markov models
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
EEG estimates of engagement and cognitive workload predict math problem solving outcomes
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Teaching data structures with beSocratic
Proceedings of the 18th ACM conference on Innovation and technology in computer science education
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The current paper focuses on modeling actions of high school students with a mathematics tutoring system with Hidden Markov Models. The results indicated that including a hidden state estimate of learner engagement increased the accuracy and predictive power of the models, both within and across tutoring sessions. Groups of students with distinct engagement trajectories were identified, and findings were replicated in two independent samples. These results suggest that modeling learner engagement may help to increase the effectiveness of intelligent tutoring systems since it was observed that engagement trajectories were not predicted by prior math achievement of students.