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Improving User Confidence in Concept Maps: Exploring Data Driven Explanations

Le Bras, Pierre; Robb, David A.; Methven, Thomas S.; Padilla, Stefano; Chantler, Mike J.

Authors

Pierre Le Bras

David A. Robb

Thomas S. Methven

Stefano Padilla

Mike J. Chantler



Abstract

Automated tools are increasingly being used to generate highly engaging concept maps as an aid to strategic planning and other decision-making tasks. Unless stakeholders can understand the principles of the underlying layout process, however, we have found that they lack confidence and are therefore reluctant to use these maps. In this paper, we present a qualitative study exploring the effect on users’ confidence of using data-driven explanation mechanisms, by conducting in-depth scenario-based interviews with ten participants. To provide diversity in stimulus and approach we use two explanation mechanisms based on projection and agglomerative layout
methods. The themes exposed in our results indicate that the data-driven explanations improved user confidence in several ways, and that process clarity and layout density also affected users’ views of the credibility of the concept maps. We discuss how these factors can increase uptake of automated tools and
affect user confidence.

Presentation Conference Type Conference Paper (Published)
Conference Name 2018 ACM CHI Conference on Human Factors in Computing Systems
Start Date Apr 21, 2018
End Date Apr 26, 2018
Acceptance Date Dec 11, 2017
Publication Date Apr 21, 2018
Deposit Date Oct 23, 2018
Publicly Available Date Oct 24, 2018
Publisher Association for Computing Machinery (ACM)
Book Title CHI '18 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
ISBN 9781450356206
DOI https://doi.org/10.1145/3173574.3173978
Keywords User Confidence; Concept Map; Data Driven Explanation; Qualitative Study
Public URL http://researchrepository.napier.ac.uk/Output/1320980
Contract Date Oct 23, 2018

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Copyright Statement
© ACM, 2018. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Le Bras, P., Robb, D. A., Methven, T. S., Padilla, S., & Chantler, M. J. (2018). Improving User Confidence in Concept Maps: Exploring Data Driven Explanations. In CHI '18 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systemsdoi:10.1145/3173574.3173978






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