Student preferences for visualising uncertainty in open learner models

Lamiya Al-Shanfari*, Chris Baber, Carrie Demmans Epp

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

User preferences for indicating uncertainty using specific visual variables have been explored outside of educational reporting. Exploring students’ preferred method to indicate uncertainty in open learner models can provide hints about which approaches students will use, so further design approaches can be considered. Participants were 67 students exploring 6 visual variables applied to a learner model visualisation (skill meter). Student preferences were ordered along a scale, which showed the size, numerosity, orientation and added marks visual variables were near one another in the learner’s preference space. Results of statistical analyses revealed differences in student preferences for some variables with opacity being the most preferred and arrangement the least preferred. This result provides initial guidelines for open learner model and learning dashboard designers to represent uncertainty information using students’ preferred method of visualisation.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings
PublisherSpringer Verlag
Pages445-449
Number of pages5
Volume10331 LNAI
ISBN (Electronic)9783319614250
ISBN (Print)9783319614243
DOIs
Publication statusPublished - 23 Jun 2017
Event18th International Conference on Artificial Intelligence in Education, AIED 2017 - Wuhan, China
Duration: 28 Jun 20171 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10331 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Education, AIED 2017
Country/TerritoryChina
CityWuhan
Period28/06/171/07/17

Bibliographical note

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10331). Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10331).

Keywords

  • Dashboards
  • Open learner models
  • Uncertainty
  • Visualisation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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