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.
|Title of host publication||Artificial Intelligence in Education|
|Subtitle of host publication||18th International Conference, AIED 2017, Wuhan, China, June 28 – July 1, 2017, Proceedings|
|Number of pages||5|
|Publication status||Published - 23 Jun 2017|
|Event||18th International Conference on Artificial Intelligence in Education, AIED 2017 - Wuhan, China|
Duration: 28 Jun 2017 → 1 Jul 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||18th International Conference on Artificial Intelligence in Education, AIED 2017|
|Period||28/06/17 → 1/07/17|
Bibliographical notePart 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).
- Open learner models
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)