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Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements

Michael Wißner, Floris Linnebank, Jochem Liem, Bert Bredeweg, Elisabeth André

Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements

erschienen 2013 Proceedings of the 16th International Conference on Artificial Intelligence in Education, pages 729-732

Verlag: Springer


This paper presents a domain independent question generation and interaction procedure that automatically generates multiple-choice questions for conceptual models created with Qualitative Reasoning vocabulary. A Bayesian Network is deployed that captures the learning progress based on the answers provided by the learner. The likelihood of concepts being known or unknown on behalf of the learner determines the focus, and the question generator adjusts the contents of its questions accordingly. As a use case, the Quiz mode is introduced.