Interaction tactics for socially intelligent pedagogical agents
Proceedings of the 8th international conference on Intelligent user interfaces
Informing the Detection of the Students' Motivational State: An Empirical Study
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Choosing when to interact with learners
Proceedings of the 9th international conference on Intelligent user interfaces
Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments
Proceedings of the 11th international conference on Intelligent user interfaces
Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation
Knowledge-Based Systems
Eliciting Motivation Knowledge from Log Files Towards Motivation Diagnosis for Adaptive Systems
UM '07 Proceedings of the 11th international conference on User Modeling
Eliciting Adaptation Knowledge from On-Line Tutors to Increase Motivation
UM '07 Proceedings of the 11th international conference on User Modeling
Relating Machine Estimates of Students' Learning Goals to Learning Outcomes: A DBN Approach
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Modeling learning patterns of students with a tutoring system using Hidden Markov Models
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Motivationally Intelligent Systems: Diagnosis and Feedback
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Learning Engagement: What Actions of Learners Could Best Predict It?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Investigating the Utility of Eye-Tracking Information on Affect and Reasoning for User Modeling
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Using eye-tracking data for high-level user modeling in adaptive interfaces
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Log file analysis for disengagement detection in e-Learning environments
User Modeling and User-Adapted Interaction
A Model to Manage Learner's Motivation: A Use-Case for an Academic Schooling Intelligent Assistant
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Assessment of motivation in online learning environments
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Intervention strategies to increase self-efficacy and self-regulation in adaptive on-line learning
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Raising confidence levels using motivational contingency design techniques
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Cross-system validation of engagement prediction from log files
EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale
Using MotSaRT to support on-line teachers in student motivation
EC-TEL'07 Proceedings of the Second European conference on Technology Enhanced Learning: creating new learning experiences on a global scale
Proceedings of the 19th international conference on Intelligent User Interfaces
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It is important for pedagogical agents to have the ability to detect the learner's motivational states. With this ability, agents will be more sensitive to the cognitive and emotional states of the learner and be able to promote the learner's motivation through interaction with the learner. In this paper we present a method for agents to assess learner's motivational states in an interactive learning environment. It takes into account the learner's attention, current task and expected time to perform the task. An experiment was conducted to collect data for evaluating the performance of the method, and the results showed that there is more than 75% to detect the learner's motivational states where intervention is warranted.