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Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Ubisoft Massive, Consumer Experience, User Research. (DDS)ORCID iD: 0000-0002-6016-028X
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2019 (English)In: 2019 IEEE Conference on Games (CoG), 2019, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
2019. p. 1-8
Keywords [en]
computer games, human factors, learning (artificial intelligence), support vector machines, gameplay data, Ubisoft Perceived Experience Questionnaire, preference learning methods, gameplay features, player motivation, motivation modelling, Tom Clancy The Division game, Games, Predictive models, Data models, Psychology, Tools, Testing, Data processing, Self-determination theory, affective computing, digital games, player modelling, preference learning
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:mau:diva-17328DOI: 10.1109/CIG.2019.8848123ISBN: 978-1-7281-1884-0 (electronic)ISBN: 978-1-7281-1885-7 (print)OAI: oai:DiVA.org:mau-17328DiVA, id: diva2:1431004
Conference
2019 IEEE Conference on Games (CoG), 20-23 Aug. 2019, London UK
Available from: 2020-05-18 Created: 2020-05-18 Last updated: 2020-05-20Bibliographically approved

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Azadvar, Ahmad
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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