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Live Feedback for Training Through Real-Time Data Visualizations: A Study with League of Legends

Authors:

Frans Rijnders,

Günter Wallner,

Regina BernhauptAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CHI PLAY

Article No.: 243, Pages 1 - 23

https://doi.org/10.1145/3549506

Published: 31 October 2022 Publication History

Abstract

With competitive gaming on the rise, players have developed an increased interest in data-driven feedback of their in-game performance. In many cases, this feedback is presented visually post-play but live companions that provide real-time analytics are gaining more and more attention. However, the design of such systems poses different requirements which have been little explored from a scholarly point of view.

In this paper, we report on a study of two visualizations - a physical and digital one - providing live feedback to League of Legends players designed based on a community survey (N= 175) and expert input. We further discuss results of a qualitative evaluation with 13 players and an expert, touch upon potential benefits and drawbacks of physical and digital implementations, and present derived design considerations and challenges for such real-time data visualizations, revolving around issues of information density, contextualization, representation, and a diverse range of user needs.

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Live Feedback for Training Through Real-Time Data Visualizations: A Study with League of Legends

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CHI PLAY

CHI PLAY

October 2022

986 pages

EISSN:2573-0142

DOI:10.1145/3570219

Editor:
Jeff Nichols
Apple Inc., United States

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Published: 31 October 2022

Published in PACMHCI Volume 6, Issue CHI PLAY

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Cited By

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https://doi.org/10.2478/pcssr-2024-0021
Hammad NHarpstead EHammer J(2024)Towards a Design Framework for Data-Driven Game Streaming: A Multi-Stakeholder ApproachProceedings of the ACM on Human-Computer Interaction10.1145/36771078:CHI PLAY(1-28)Online publication date: 15-Oct-2024
https://dl.acm.org/doi/10.1145/3677107
Wang LDormann CWallner G(2024)A Feature Comparison Study of Live Companion Tools for Esports GamesProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650004(1-11)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650004
Lee HLee SNallapati RUh YLee B(2024)Characterizing and Quantifying Expert Input Behavior in League of LegendsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642588(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642588
Barbosa CSousa TMonteiro WAraújo TMeiguins B(2023)Enhancing Color Scales for Active Data PhysicalizationsApplied Sciences10.3390/app1401016614:1(166)Online publication date: 24-Dec-2023
https://doi.org/10.3390/app14010166
Wallner GWang LDormann C(2023)Visualizing the Spatio-Temporal Evolution of Gameplay using Storyline Visualization: A Study with League of LegendsProceedings of the ACM on Human-Computer Interaction10.1145/36110587:CHI PLAY(1002-1024)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3611058

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