Improving User Confidence in Concept Maps: Exploring Data Driven Explanations

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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.
Original languageEnglish
Title of host publicationProceedings of the 2018 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
ISBN (Print)9781450356206
DOIs
Publication statusPublished - 21 Apr 2018
Event2018 ACM CHI Conference on Human Factors in Computing Systems - Palais des Congrès de Montréal, Montreal, Canada
Duration: 21 Apr 201826 Apr 2018
https://chi2018.acm.org/

Conference

Conference2018 ACM CHI Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI 2018
CountryCanada
CityMontreal
Period21/04/1826/04/18
Internet address

Keywords

  • User Confidence
  • Concept Map
  • Data Driven Explanation;
  • Qualitative Study

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    Le Bras, P., Robb, D., Methven, T., Padilla, S., & Chantler, M. J. (2018). Improving User Confidence in Concept Maps: Exploring Data Driven Explanations. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems [404] Association for Computing Machinery. https://doi.org/10.1145/3173574.3173978