Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Applying Plan Recognition Algorithms To Program Understanding
Automated Software Engineering
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
A collaborative planning model of intentional structure
Computational Linguistics
Towards Collaborative Intelligent Tutors: Automated Recognition of Users' Strategies
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
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
On natural language processing and plan recognition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Plan Recognition and Visualization in Exploratory Learning Environments
ACM Transactions on Interactive Intelligent Systems (TiiS)
Visualizing expert solutions in exploratory learning environments using plan recognition
Proceedings of the 19th international conference on Intelligent User Interfaces
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This paper presents a plan recognition algorithm for inferring student behavior using virtual science laboratories. The algorithm extends existing plan recognition technology and was integrated with an existing educational application for chemistry. Automatic recognition of students' activities in virtual laboratories can provide important information to teachers as well as serve as the basis for intelligent tutoring. Student use of virtual laboratories presents several challenges: Students may repeat activities indefinitely, interleave between activities, and engage in exploratory behavior using trial-and-error. The plan recognition algorithm uses a recursive grammar that heuristically generates plans on the fly, taking into account chemical reactions and effects to determine students' intended high-level actions. The algorithm was evaluated empirically on data obtained from college students using virtual laboratory software for teaching chemistry. Results show that the algorithm was able to (1) infer the plans used by students to construct their models; (2) recognize such key processes as titration and dilution when they occurred in students' work; (3) identify partial solutions; (4) isolate sequences of actions that were part of a single error.