User cognitive style and interface design for personal, adaptive learning, what to model?

Elizabeth Uruchrutu, Lachlan MacKinnon, Roger Rist

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

6 Citations (Scopus)

Abstract

The concept of personal learning environments has become a significant research topic over the past few years. Building such personal, adaptive environments requires the convergence of several modeling dimensions and an interaction strategy based on a user model that incorporates key cognitive characteristics of the learners. This paper reports on an initial study carried out to evaluate the extent to which matching the interface design to the learner cognitive style facilitates learning performance. Results show that individual differences influence the way learners react to and perform under different interface conditions, however no simple effects were observed that confirm a relationship between cognitive style and interface affect. © Springer-Verlag Berlin Heidelberg 2005.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages154-163
Number of pages10
Volume3538 LNAI
Publication statusPublished - 2005
Event10th International Conference on User Modeling - Edinburgh, Scotland, United Kingdom
Duration: 24 Jul 200529 Jul 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3538 LNAI
ISSN (Print)0302-9743

Conference

Conference10th International Conference on User Modeling
Abbreviated titleUM 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period24/07/0529/07/05

Cite this

Uruchrutu, E., MacKinnon, L., & Rist, R. (2005). User cognitive style and interface design for personal, adaptive learning, what to model? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3538 LNAI, pp. 154-163). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3538 LNAI).