Improving User Confidence in Concept Maps: Exploring Data Driven Explanations

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

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