Improving User Confidence in Concept Maps: Exploring Data Driven Explanations

Pierre Le Bras; David Robb; Thomas Methven; Stefano Padilla; Michael John Chantler

doi:10.1145/3173574.3173978

Improving User Confidence in Concept Maps: Exploring Data Driven Explanations

Pierre Le Bras, David Robb, Thomas Methven, Stefano Padilla, Michael John Chantler

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Citations (Scopus)

2150 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
ISBN (Print)	9781450356206
DOIs	https://doi.org/10.1145/3173574.3173978
Publication status	Published - 21 Apr 2018
Event	2018 ACM CHI Conference on Human Factors in Computing Systems - Palais des Congrès de Montréal, Montreal, Canada Duration: 21 Apr 2018 → 26 Apr 2018 https://chi2018.acm.org/

Conference

Conference	2018 ACM CHI Conference on Human Factors in Computing Systems
Abbreviated title	ACM CHI 2018
Country/Territory	Canada
City	Montreal
Period	21/04/18 → 26/04/18
Internet address	https://chi2018.acm.org/

Keywords

User Confidence
Concept Map
Data Driven Explanation;
Qualitative Study

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10.1145/3173574.3173978

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© ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CHI 2018, April 21–26, 2018, Montreal, QC, Canada, http://dx.doi.org/10.1145/3173574.3173978
Accepted author manuscript, 1.96 MBLicence: All Rights Reserved

Stefano Padilla
- s.padillahw.acuk
- School of Mathematical & Computer Sciences - Associate Professor
- School of Mathematical & Computer Sciences, Computer Science - Associate Professor
Person: Academic (Research & Teaching)

Cite this

@inproceedings{031478510bd34ff0b4955dfc33a4dc02,

title = "Improving User Confidence in Concept Maps: Exploring Data Driven Explanations",

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{\textquoteright} 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 layoutmethods. 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{\textquoteright} views of the credibility of the concept maps. We discuss how these factors can increase uptake of automated tools andaffect user confidence.",

keywords = "User Confidence, Concept Map, Data Driven Explanation;, Qualitative Study",

author = "{Le Bras}, Pierre and David Robb and Thomas Methven and Stefano Padilla and Chantler, {Michael John}",

year = "2018",

month = apr,

day = "21",

doi = "10.1145/3173574.3173978",

language = "English",

isbn = "9781450356206",

booktitle = "Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery",

address = "United States",

note = "2018 ACM CHI Conference on Human Factors in Computing Systems, ACM CHI 2018 ; Conference date: 21-04-2018 Through 26-04-2018",

url = "https://chi2018.acm.org/",

}

Le Bras, P , Robb, D , Methven, T , Padilla, S & Chantler, MJ 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, 2018 ACM CHI Conference on Human Factors in Computing Systems, Montreal, Canada, 21/04/18. https://doi.org/10.1145/3173574.3173978

TY - GEN

T1 - Improving User Confidence in Concept Maps

T2 - 2018 ACM CHI Conference on Human Factors in Computing Systems

AU - Le Bras, Pierre

AU - Robb, David

AU - Methven, Thomas

AU - Padilla, Stefano

AU - Chantler, Michael John

PY - 2018/4/21

Y1 - 2018/4/21

N2 - 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 layoutmethods. 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 andaffect user confidence.

AB - 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 layoutmethods. 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 andaffect user confidence.

KW - User Confidence

KW - Concept Map

KW - Data Driven Explanation;

KW - Qualitative Study

U2 - 10.1145/3173574.3173978

DO - 10.1145/3173574.3173978

M3 - Conference contribution

SN - 9781450356206

BT - Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

Y2 - 21 April 2018 through 26 April 2018

ER -

Improving User Confidence in Concept Maps: Exploring Data Driven Explanations

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Stefano Padilla

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