@inproceedings{ad2a0ef174194509a1ed12a9b0731c9f,
title = "Student preferences for visualising uncertainty in open learner models",
abstract = "User preferences for indicating uncertainty using specific visual variables have been explored outside of educational reporting. Exploring students{\textquoteright} 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{\textquoteright}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{\textquoteright} preferred method of visualisation.",
keywords = "Dashboards, Open learner models, Uncertainty, Visualisation",
author = "Lamiya Al-Shanfari and Chris Baber and {Demmans Epp}, Carrie",
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).; 18th International Conference on Artificial Intelligence in Education, AIED 2017 ; Conference date: 28-06-2017 Through 01-07-2017",
year = "2017",
month = jun,
day = "23",
doi = "10.1007/978-3-319-61425-0_37",
language = "English",
isbn = "9783319614243",
volume = "10331 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "445--449",
booktitle = "Artificial Intelligence in Education",
}