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 comparative analysis of cognitive tutoring and constraint-based modeling
UM'03 Proceedings of the 9th international conference on User modeling
Data-driven student knowledge assessment through ill-defined procedural tasks
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Fifteen years of constraint-based tutors: what we have achieved and where we are going
User Modeling and User-Adapted Interaction
Exploring quality of constraints for assessment in problem solving environments
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Computer-assisted assessment with item classification for programming skills
Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality
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One of the most popular student modeling techniques currently available is Constraint Based Modeling (CBM), which is based on Ohlsson's theory of learning from performance errors. It focuses on the domain principles to correct faulty knowledge and assumes that a student will reach a correct solution without violating these fundamental domain concepts. However, even though this is a powerful and computationally simple technique, most student models of CBM-based tutors handle simple long-term models or based on heuristics to quantitatively estimate the knowledge measured. In this paper we propose a student knowledge diagnosis model which combines CBM with the Item Response Theory (IRT). IRT is a probabilistic and data-driven theory which guarantees accurate and invariant student knowledge estimations. By means of this synergy between CBM and IRT we suggest the construction of long-term student models composed of the estimations of their knowledge. This paper also includes an experiment we have carried out with real students, which explores the validity of the diagnoses made with our model.