A model for reasoning about persistence and causation
Computational Intelligence
Use of analogy in a production system architecture
Similarity and analogical reasoning
Individual selection of examples in an intelligent learning environment
Journal of Artificial Intelligence in Education
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
The Ambre ILE: How to Use Case-Based Reasoning to Teach Methods
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Tailored scaffolding for meta-cognitive skills during analogical problem solving
Tailored scaffolding for meta-cognitive skills during analogical problem solving
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
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
The Andes Physics Tutoring System: Five Years of Evaluations
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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
Evaluating a decision-theoretic approach to Tailored example selection
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using similarity to infer meta-cognitive behaviors during analogical problem solving
UM'05 Proceedings of the 10th international conference on User Modeling
Exploring eye tracking to increase bandwidth in user modeling
UM'05 Proceedings of the 10th international conference on User Modeling
“Yes!”: using tutor and sensor data to predict moments of delight during instructional activities
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
A decision-theoretic approach to scientific inquiry exploratory learning environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Adapting to when students game an intelligent tutoring system
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Generalizing detection of gaming the system across a tutoring curriculum
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Scaffolding problem solving with annotated, worked-out examples to promote deep learning
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
An analysis of students' gaming behaviors in an intelligent tutoring system: predictors and impacts
User Modeling and User-Adapted Interaction
Support options provided and required for modeling with DynaLearn--A case study
Education and Information Technologies
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Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples effectively. The Example Analogy (EA)-Coach is an Intelligent Tutoring System that provides adaptive support to foster meta-cognitive behaviors relevant to a specific type of example-based learning known as analogical problem solving (APS), i.e., using examples to aid problem solving. To encourage the target meta-cognitive behaviors, the EA-Coach provides multiple levels of scaffolding, including an innovative example-selection mechanism that chooses examples with the best potential to trigger learning and enable problem solving for a given student. To find such examples, the mechanism relies on our novel classification of problem/ example differences and associated hypotheses regarding their impact on the APS process. Here, we focus on describing (1) how the overall design of the EA-Coach in general, and the example-selection mechanism in particular, evolved from cognitive science research on APS; (2) our pilot evaluations and the controlled laboratory study we conducted to validate the tutor's pedagogical utility. Our results show that the EA-Coach fosters meta-cognitive behaviors needed for effective learning during APS, while helping students achieve problem-solving success.