Machine Learning - Special issue on learning with probabilistic representations
Knowledge Spaces
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Student Modeling from Conversational Test Data: A Bayesian Approach Without Priors
ITS '98 Proceedings of the 4th 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
Applying Competence Structures for Peer Tutor Recommendations in CSCL Environments
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
ROCR: visualizing classifier performance in R
Bioinformatics
Learned student models with item to item knowledge structures
User Modeling and User-Adapted Interaction
Data Mining
Unsupervised and supervised machine learning in user modeling for intelligent learning environments
Proceedings of the 12th international conference on Intelligent user interfaces
A Bayesian Student Model without Hidden Nodes and its Comparison with Item Response Theory
International Journal of Artificial Intelligence in Education
International Journal of Artificial Intelligence in Education
Extracting student models for intelligent tutoring systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Introducing prerequisite relations in a multi-layered bayesian student model
UM'05 Proceedings of the 10th international conference on User Modeling
The assessment of knowledge, in theory and in practice
ICFCA'06 Proceedings of the 4th international conference on Formal Concept Analysis
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
Assessing a learner's mastery of a set of skills is a fundamental issue in intelligent learning environments. We compare the predictive performance of two approaches for training a learner model with domain data. One is based on the principle of building the model solely from observable data items, such as exercises or test items. Skills modelling is not part of the training phase, but instead dealt with at later stage. The other approach incorporates a single latent skill in the model. We compare the capacity of both approaches to accurately predict item outcome (binary success or failure) from a subset of item outcomes. Three types of item-to-item models based on standard Bayesian modeling algorithms are tested: (1) Naive Bayes, (2) Tree-Augmented Naive Bayes (TAN), and (3) a K2 Bayesian Classifier. Their performance is compared to the widely used IRT-2PL approach which incorporates a single latent skill. The results show that the item-to-item approaches perform as well, or better than the IRT-2PL approach over 4 widely different data sets, but the differences vary considerably among the data sets. We discuss the implications of these results and the issues relating to the practical use of item-to-item models.