research-article

Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division

Authors:

Alessandro Canossa,

Antonios Liapis,

Georgios N. YannakakisAuthors Info & Claims

2019 IEEE Conference on Games (CoG)

Pages 1 - 8

https://doi.org/10.1109/CIG.2019.8848123

Published: 20 August 2019 Publication History

Abstract

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy’s The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.

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Cited By

Canossa AAzadvar AZhu JHarteveld CPirker J(2024)Country as a proxy for culture? An exploratory study of players in an Online Multiplayer GameExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650992(1-7)Online publication date: 11-May-2024
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Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division

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cover image Guide Proceedings

2019 IEEE Conference on Games (CoG)

Aug 2019

1060 pages

Copyright © 2019.

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IEEE Press

Publication History

Published: 20 August 2019

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Cited By

Canossa AAzadvar AZhu JHarteveld CPirker J(2024)Country as a proxy for culture? An exploratory study of players in an Online Multiplayer GameExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650992(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650992
Carneiro NSouza KSidarta IPereira GMendonça GDarin TMonteiro Ida Hora Rodrigues Kde Gois Ribeiro Darin TFreire AMota M(2021)To Each Their Own (Type)Proceedings of the XX Brazilian Symposium on Human Factors in Computing Systems10.1145/3472301.3484352(1-12)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3472301.3484352
Meyer S(2021)Natural Language Stage of Change Modelling for “Motivationally-driven” Weight Loss SupportProceedings of the 2021 International Conference on Multimodal Interaction10.1145/3462244.3481279(807-811)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3462244.3481279
Melhart DGravina DYannakakis G(2020)Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on TwitchProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3402958(1-10)Online publication date: 15-Sep-2020
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Moon HSeo JIqbal SMacLean KChevalier FMueller S(2020)Dynamic Difficulty Adjustment via Fast User AdaptationAdjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology10.1145/3379350.3418578(13-15)Online publication date: 20-Oct-2020
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