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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Wißner, M., Linnebank, F., Liem, J., Bredeweg, B., & André, E. (2013). Question generation and adaptation using a bayesian network of the learner’s achievements. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7926 LNAI, pp. 729–732). Springer Verlag. https://doi.org/10.1007/978-3-642-39112-5_99
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