Improving Student Performance Using Self-Assessment Tests
IEEE Intelligent Systems
International Journal of Artificial Intelligence in Education
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
A Probabilistic Model for Student Knowledge Diagnosis in Learning Environments
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Collaborative assessment with SIETTE
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
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
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Student procedural knowledge inference through item response theory
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
A data-driven technique for misconception elicitation
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
Towards social mobile blended learning
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
A web based collaborative testing environment
Computers & Education
An empirical study on the quantitative notion of task difficulty
Expert Systems with Applications: An International Journal
CAT model with personalized algorithm for evaluation of estimated student knowledge
Education and Information Technologies
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This paper presents an approach to student modeling in which knowledge is represented by means of probability distributions associated to a tree of concepts. A diagnosis procedure which uses adaptive testing is part of this approach. Adaptive tests provide well-founded and accurate diagnosis thanks to the underlying probabilistic theory, i.e., the Item Response Theory. Most adaptive testing proposals are based on dichotomous models, where he student answer can only be considered either correct or incorrect. In the work described here, a polytomous model has been used, i.e., answers can be given partial credits. Thus, models are more informative and diagnosis is more efficient. This paper also presents an algorithm for estimating question characteristic curves, which are necessary in order to apply the Item Response Theory to a given domain and hence must be inferred before testing begins. Most prior estimation procedures need huge sets of data. We have modified preexisting procedures in such a way that data requirements are significantly reduced. Finally, this paper presents the results of some controlled evaluations that have been carried out in order to analyze the feasibility and advantages of this approach.