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

David Melhart; Ahmad Azadvar; Alessandro Canossa; Antonios Liapis; Georgios N Yannakakis

doi:10.1109/CIG.2019.8848123

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

David Melhart, Ahmad Azadvar, Alessandro Canossa, Antonios Liapis, Georgios N Yannakakis

Publications: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	2019 IEEE Conference on Games (CoG)
Number of pages	8
Publisher	IEEE
Publication date	2019
Pages	1-8
DOIs	https://doi.org/10.1109/CIG.2019.8848123
Publication status	Published - 2019
Externally published	Yes
Event	2019 IEEE Conference on Games (CoG) - London, United Kingdom Duration: 20 Aug 2019 → 23 Aug 2019

Conference

Conference	2019 IEEE Conference on Games (CoG)
Country/Territory	United Kingdom
City	London
Period	20/08/2019 → 23/08/2019

Artistic research

No

Access to Document

10.1109/CIG.2019.8848123

Cite this

@inproceedings{93f0dff2f82f4c35aa502e00736e91f6,

title = "Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division",

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.",

author = "David Melhart and Ahmad Azadvar and Alessandro Canossa and Antonios Liapis and Yannakakis, {Georgios N}",

year = "2019",

doi = "10.1109/CIG.2019.8848123",

language = "English",

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booktitle = "2019 IEEE Conference on Games (CoG)",

publisher = "IEEE",

note = "2019 IEEE Conference on Games (CoG) ; Conference date: 20-08-2019 Through 23-08-2019",

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AU - Canossa, Alessandro

AU - Liapis, Antonios

AU - Yannakakis, Georgios N

PY - 2019

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N2 - 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.

AB - 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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