A formal theory of plan recognition
A formal theory of plan recognition
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Modeling Student Knowledge: Cognitive Tutors in High School and College
User Modeling and User-Adapted Interaction
Using a Learning Agent with a Student Model
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
A collaborative planning model of intentional structure
Computational Linguistics
Learning and inferring transportation routines
Artificial Intelligence
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
Generalizing detection of gaming the system across a tutoring curriculum
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Recognition of Users' Activities Using Constraint Satisfaction
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Plan recognition in exploratory domains
Artificial Intelligence
Plan recognition in virtual laboratories
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Proceedings of the 2013 conference on Computer supported cooperative work
Plan Recognition and Visualization in Exploratory Learning Environments
ACM Transactions on Interactive Intelligent Systems (TiiS)
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This paper addresses the problem of inferring students' strategies when they interact with data-modeling software used for pedagogical purposes. The software enables students to learn about statistical data by building and analyzing their own models. Automatic recognition of students' activities when interacting with pedagogical software is challenging. Students can pursue several plans in parallel and interleave the execution of these plans. The algorithm presented in this paper decomposes students' complete interaction histories with the software into hierarchies of interdependent tasks that may be subsequently compared with ideal solutions. This algorithm is evaluated empirically using commercial software that is used in many schools. Results indicate that the algorithm is able to (1) identify the plans students use when solving problems using the software; (2) distinguish between those actions in students' plans that play a salient part in their problem-solving and those representing exploratory actions and mistakes; and (3) capture students' interleaving and free-order action sequences.