Student preferences for visualising uncertainty in open learner models

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

Authors

  • Lamiya Al-Shanfari
  • Chris Baber
  • Carrie Demmans Epp

Colleges, School and Institutes

External organisations

  • University of Pittsburgh

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.

Bibliographic 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).

Details

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
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

Keywords

  • Dashboards, Open learner models, Uncertainty, Visualisation