User-test results injection into task-based design process for the assessment and improvement of both usability and user experience

10Citations
Citations of this article
23Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

User Centered Design processes argue for user testing in order to assess and improve the quality of the interactive systems developed. The underlying belief is that the findings from user testing related to usability and user experience will inform the design of the interactive system in a relevant manner. Unfortunately reports from the industrial practice indicate that this is not straightforward and a lot of data gathered during user tests is hard to understand and exploit. This paper claims that injecting results from user-tests in user-tasks descriptions support the exploitation of user test results for designing the n+1 prototype. In order to do so, the paper proposes a set of extensions to current task description techniques and a process for systematically populating task models with data and analysis gathered during user testing. Beyond the already known advantages of task models, these enriched task models provide additional benefits in different phases of the development process. For instance, it is possible to go beyond standard task-model based performance evaluation exploiting real performance data from usability evaluation. Additionally, it also supports taskmodel based comparisons of two alternative systems. It can also support performance prediction and overall supports identification of usability problems and identifies shortcomings for user experience. The application of such a process is demonstrated on a case study from the interactive television domain.

Cite

CITATION STYLE

APA

Bernhaupt, R., Palanque, P., Manciet, F., & Martinie, C. (2016). User-test results injection into task-based design process for the assessment and improvement of both usability and user experience. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9856 LNCS, pp. 56–72). Springer Verlag. https://doi.org/10.1007/978-3-319-44902-9_5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free