Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Knowledge assessment: tapping human expertise by the QUERY routine
International Journal of Human-Computer Studies
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Web-based education for all: a tool for development adaptive courseware
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
IEEE Transactions on Knowledge and Data Engineering
Adaptive Assessment Using Granularity Hierarchies and Bayesian Nets
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Bayesian networks in educational testing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
SIETTE: A Web-Based Tool for Adaptive Testing
International Journal of Artificial Intelligence in Education
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
Interacting with Inspectable Bayesian Student Models
International Journal of Artificial Intelligence in Education
The Andes Physics Tutoring System: Five Years of Evaluations
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Adaptation and generation in a web-based tutor for linear programming
ICWE'03 Proceedings of the 2003 international conference on Web engineering
Student modeling for a web-based self-assessment system
Expert Systems with Applications: An International Journal
Performance comparison of item-to-item skills models with the IRT single latent trait model
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Bayesian student models based on item to item knowledge structures
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Improving matrix factorization techniques of student test data with partial order constraints
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
CAT model with personalized algorithm for evaluation of estimated student knowledge
Education and Information Technologies
Hi-index | 0.00 |
The Bayesian framework offers a number of techniques for inferring an individual's knowledge state from evidence of mastery of concepts or skills. A typical application where such a technique can be useful is Computer Adaptive Testing (CAT). A Bayesian modeling scheme, POKS, is proposed and compared to the traditional Item Response Theory (IRT), which has been the prevalent CAT approach for the last three decades. POKS is based on the theory of knowledge spaces and constructs item-to-item graph structures without hidden nodes. It aims to offer an effective knowledge assessment method with an efficient algorithm for learning the graph structure from data. We review the different Bayesian approaches to modeling student ability assessment and discuss how POKS relates to them. The performance of POKS is compared to the IRT two parameter logistic model. Experimental results over a 34 item Unix test and a 160 item French language test show that both approaches can classify examinees as master or non-master effectively and efficiently, with relatively comparable performance. However, more significant differences are found in favor of POKS for a second task that consists in predicting individual question item outcome. Implications of these results for adaptive testing and student modeling are discussed, as well as the limitations and advantages of POKS, namely the issue of integrating concepts into its structure.