Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
Journal of Automated Reasoning
Looking Ahead to Select Tutorial Actions: A Decision-Theoretic Approach
International Journal of Artificial Intelligence in Education
Analyzing Completeness and Correctness of Utterances Using an ATMS
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Short answer assessment: establishing links between research strands
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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We describe a combination of a statistical and symbolic approaches for automated scoring of student utterances according to their semantic content. The proposed semantic classifier overcomes the limitations of bag-of-words methods by mapping natural language sentences into predicate representations and matching them against the automatically generated deductive closure of the domain givens, buggy assumptions and domain rules. With the goal to account for uncertainties in both symbolic representations of natural language sentences and logical relations between domain statements, this work extends the deterministic symbolic approach by augmenting the deductive closure graph structure with conditional probabilities, thus creating a Bayesian network. By deriving the structure of the network formally, instead of estimating it from data, we alleviate the problem of sparseness of training data. We compare the performance of the Bayesian network classifier with the deterministic graph matching-based classifiers and baselines.