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

M. Wißner; F. Linnebank; J. Liem; B. Bredeweg; E. André

doi:10.1007/978-3-642-39112-5_99

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

M. Wißner, F. Linnebank, J. Liem, B. Bredeweg, E. André

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	Artificial Intelligence in Education
Editors	H.C. Lane, K. Yacef, J. Mostow, P. Pavlik
Publisher	Springer
Pages	729-732
ISBN (Print)	9783642391118
DOIs	https://doi.org/10.1007/978-3-642-39112-5_99
Publication status	Published - 2013

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Name	Lecture Notes in Computer Science
Publisher	Springer

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title = "Question Generation and Adaptation Using a Bayesian Network of the Learner{\textquoteright}s Achievements",

abstract = "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.",

author = "M. Wi{\ss}ner and F. Linnebank and J. Liem and B. Bredeweg and E. Andr{\'e}",

note = "AIED 2013, Memphis, TN, USA ; Conference date: 01-01-2013",

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doi = "10.1007/978-3-642-39112-5_99",

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Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements. / Wißner, M.; Linnebank, F.; Liem, J. et al.
Artificial Intelligence in Education. ed. / H.C. Lane; K. Yacef; J. Mostow; P. Pavlik. Springer, 2013. p. 729-732 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Bredeweg, B.

AU - André, E.

N1 - AIED 2013, Memphis, TN, USA ; Conference date: 01-01-2013

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-39112-5_99

DO - 10.1007/978-3-642-39112-5_99

M3 - Conference contribution

SN - 9783642391118

T3 - Lecture Notes in Computer Science

SP - 729

EP - 732

BT - Artificial Intelligence in Education

A2 - Lane, H.C.

A2 - Yacef, K.

A2 - Mostow, J.

A2 - Pavlik, P.

PB - Springer

ER -

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

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