Developing a Bayes-net based student model for an External Representation Selection Tutor

Beate Grawemeyer, Richard Cox

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

This paper describes the process by which we are constructing an intelligent tutoring system (ERST) designed to improve learners' external representation (ER) selection accuracy on a range of database query tasks. This paper describes how ERST's student model is being constructed - it is a Bayesian network seeded with data from experimental studies. The studies examined the effects of students' background knowledge-of-external representations (KER) upon performance and their preferences for particular information display forms across a range of database query types.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publicationSupporting Learning through Intelligent and Socially Informed Technology
EditorsChee-Kit Looi, Gord McCalla, Bert Bredeweg, Joost Breuker
PublisherIOS Press BV
Pages810-812
Number of pages3
ISBN (Electronic)1586035304, 9781586035303
Publication statusPublished - 2005
Externally publishedYes
Event12th International Conference on Artificial Intelligence in Education - Amsterdam, Netherlands
Duration: 18 Jul 200522 Jul 2005
Conference number: 12

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume125
ISSN (Print)0922-6389

Conference

Conference12th International Conference on Artificial Intelligence in Education
Abbreviated titleAIED 2005
Country/TerritoryNetherlands
CityAmsterdam
Period18/07/0522/07/05

Bibliographical note

Publisher Copyright:
© 2005 The authors. All rights reserved.

Keywords

  • Bayesian networks
  • External representations
  • Student modeling

ASJC Scopus subject areas

  • Artificial Intelligence

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