Help seeking, learning and contingent tutoring
Computers & Education
Limitations of Student Control: Do Students Know When They Need Help?
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor
International Journal of Artificial Intelligence in Education
What matters in help-seeking? A study of help effectiveness and learner-related factors
Computers in Human Behavior
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
AutoTutor: an intelligent tutoring system with mixed-initiative dialogue
IEEE Transactions on Education
A programming tutor for haskell
CEFP'11 Proceedings of the 4th Summer School conference on Central European Functional Programming School
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Help seeking behavior in an intelligent tutoring system was analyzed to identify help seeking strategies, and it was investigated whether the use of these strategies could be predicted by achievement goal scores. Discrete Markov Models and a k-means clustering algorithm were used to identify strategies, and logistic regression analyses (n = 45) were used to analyze the relation between achievement goals and strategy use. Five strategies were identified, three of which were predicted by achievement goal scores. These strategies were labeled Little Help, Click Through Help, Direct Solution, Step By Step, and Quick Solution. The Click Through Help strategy was predicted by mastery avoidance goals, the Direct Solution strategy was negatively predicted by mastery avoidance goals and positively predicted by performance avoidance goals, and the Quick Solution strategy was negatively predicted by performance approach goals.