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

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 p...

Full description

Saved in:
Bibliographic Details
Published inArtificial Intelligence in Education pp. 729 - 732
Main Authors Wißner, Michael, Linnebank, Floris, Liem, Jochem, Bredeweg, Bert, André, Elisabeth
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2013
SeriesLecture Notes in Computer Science
Subjects
Bayesian Networks
Conceptual Knowledge
Learner Models
Question Generation
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9783642391118
3642391117
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-39112-5_99