Computers as Cognitive Tools
Limitations of Student Control: Do Students Know When They Need Help?
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
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
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
Factors influencing the performance of Dynamic Decision Network for INQPRO
Computers & Education
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
Affect and Usage Choices in Simulation Problem-Solving Environments
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
The Design, Deployment and Evaluation of the AnimalWatch Intelligent Tutoring System
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Integrated introspective case-based reasoning for intelligent tutoring systems
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
The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
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Intelligent tutoring systems (ITS) can provide effective instruction, but learners do not always use such systems effectively. In the present study, high school students' action sequences with a mathematics ITS were machine-classified into five finite-state machines indicating guessing strategies, appropriate help use, and independent problem solving; over 90% of problem events were categorized. Students were grouped via cluster analyses based on self reports of motivation. Motivation grouping predicted ITS strategic approach better than prior math achievement (as rated by classroom teachers). Learners who reported being disengaged in math were most likely to exhibit appropriate help use while working with the ITS, relative to average and high motivation learners. The results indicate that learners can readily report their motivation state and that these data predict how learners interact with the ITS.