Cognitive modeling and intelligent tutoring
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Intelligence without representation
Artificial Intelligence
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Adaptive testing for hierarchical student models
User Modeling and User-Adapted Interaction
Intelligent Tutors for All: The Constraint-Based Approach
IEEE Intelligent Systems
Improving Student Performance Using Self-Assessment Tests
IEEE Intelligent Systems
A SOA-Based Framework for Constructing Problem Solving Environments
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
A blended E-learning experience in a course of object oriented programming fundamentals
Knowledge-Based Systems
Student Knowledge Diagnosis Using Item Response Theory and Constraint-Based Modeling
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Exploring quality of constraints for assessment in problem solving environments
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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The Item Response Theory (IRT) is a statistical mechanism successfully used since the beginning of the 20th century to infer student knowledge through tests. Nevertheless, existing well-founded techniques to assess procedural tasks are generally complex and applied to well-defined tasks. In this paper, we describe how, using a set of techniques we have developed based on IRT, it is possible to infer declarative student knowledge through procedural tasks. We describe how these techniques have been used with undergraduate students, in the object oriented programming domain, through ill-defined procedural exercises.