Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
A Practical Approach to Bayesian Student Modeling
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Student Modeling from Conversational Test Data: A Bayesian Approach Without Priors
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
The Conceptual Helper: An Intelligent Tutoring System for Teaching Fundamental Physics Concepts
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
A Web-Based Intelligent Tutoring System for Computer Programming
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A student model for object-oriented design and programming
Journal of Computing Sciences in Colleges
From objects-first to design-first with multimedia and intelligent tutoring
ITiCSE '05 Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education
A design-first curriculum for teaching Java in a CS1 course
ACM SIGCSE Bulletin
The Andes Physics Tutoring System: Lessons Learned
International Journal of Artificial Intelligence in Education
Introducing prerequisite relations in a multi-layered bayesian student model
UM'05 Proceedings of the 10th international conference on User Modeling
Individualizing Tutoring with Learning Style Based Feedback
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Atomic Dynamic Bayesian Networks for a Responsive Student Model
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Bayesian network to manage learner model in context-aware adaptive system in mobile learning
Edutainment'11 Proceedings of the 6th international conference on E-learning and games, edutainment technologies
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Atomic Bayesian Networks (ABNs) combine several valuable features in student models: prerequisite relationships, concept to solution step relationships, and real time responsiveness. Recent work addresses some of these features but have not combined them, which we believe is necessary in an ITS that helps students learn in a complex domain, in our case, object-oriented design. A refined representation of prerequisite relationships considers relationships between concepts as explicit knowledge units. Theorems show how to reduce the number of parameters required to a small constant, so that each ABN can guarantee a real time response. We evaluated ABN-based student models with 240 simulated students, investigating their behavior for different types of students and different slip and guess values. Holding slip and guess to equal, small values, ABNs are able to produce accurate diagnostic rates for student knowledge states.