Understanding Your Users: A Practical Guide to User Requirements Methods, Tools, and Techniques
Understanding Your Users: A Practical Guide to User Requirements Methods, Tools, and Techniques
Effects of low & high literacy on user performance in information search and retrieval
BCS-HCI '08 Proceedings of the 22nd British HCI Group Annual Conference on People and Computers: Culture, Creativity, Interaction - Volume 1
NDM'09 Proceedings of the 9th Bi-annual international conference on Naturalistic Decision Making
Mobile interface design for low-literacy populations
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Interactive visualization for low literacy users: from lessons learnt to design
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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The purpose of this paper is to report the possible reasons for premature abandonment by low-literate users during online searches. Previous evidence suggests that low-literate web users abandon their online searches early believing that the information they were looking for should be in the section they were at, thinking that they have either found it or that the information was unavailable. This paper describes an open-card sorting technique combined with multiple Cognitive Task Analysis (CTA) methods to understand why this occurs. Nine high-literate and eight low-literate volunteers of the Citizens Advice Bureau (CAB) sorted 37 cards representing information in the "Adviceguide" social services website. The qualitative data collected were analysed using Emergent Themes Analysis (ETA). Results showed that low-literate users do not create main and subgroups when classifying the cards but kept them on single-level taxonomy. They rank these groups based on flawed interpretations of concepts and personal or hypothetical experiences. High-literate users create multi-level taxonomies and their interpretations are based on keywords and interpretations of concepts and personal or hypothetical experiences. We believe these differences in classification models may contribute to premature abandonment of online searches by low-literate users.