Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Your Gameplay Says It All: Modelling Motivation in Tom Clancy’s The Division
Institute of Digital Games, University of Malta.
Ubisoft Massive, Consumer Experience, User Research.ORCID iD: 0000-0002-6016-028X
Massive Entertainment a Ubisoft Studio.
Institute of Digital Games, University of Malta.
Show others and affiliations
2019 (English)In: 2019 IEEE Conference on Games (CoG), IEEE, 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
IEEE, 2019. p. 1-8
Series
IEEE Conference on Computational Intelligence and Games, ISSN 2325-4270, E-ISSN 2325-4289
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.2019.8848123ISI: 000843154300011Scopus ID: 2-s2.0-85073118768ISBN: 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: 2024-12-12Bibliographically approved
In thesis
1. Predictive Psychological Player Profiling
Open this publication in new window or tab >>Predictive Psychological Player Profiling
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Video games have become the largest portion of the entertainment industry and everyday life of millions of players around the world. Considering games as cultural artifacts, it seems imperative to study both games and players to understand underlying psychological and behavioral implications of interacting with this medium, especially since video games are rich domains for occurrence of rich affective experiences annotated by and measurable via in-game behavior. This thesis is a presentation of a series of studies that attempt to model player perception and behavior as well as their psychosocial attributes in order to make sense of interrelations of these factors and implications the findings have for game designers and researchers. In separate studies including survey and in-game telemetry data of millions of players, we delve into reliable measures of player psychological need satisfaction, motivation and generational cohort and cross reference them with in-game behavioral patterns by presenting systemic frameworks for classification and regression. We introduce a measurement of perceived need satisfaction and discuss generational effects in playtime and motivation, present a robust prediction model for ordinally processed motivations and review classification techniques when it comes to playstyles derived from player choices. Additionally, social aspects of play, such as social influence and contagion as well as disruptive behavior, is discussed along with advanced statistical models to detect and explain them.   

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. p. 121
Series
Studies in Computer Science
Keywords
Human-Computer Interaction, Affective Computing, Player Experience, User Research, Behavioral modeling, Psychology of play
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:mau:diva-41436 (URN)
Presentation
2021-05-27, Zoom, 17:00 (English)
Supervisors
Note

Note: The papers are not included in the fulltext online

Vid tidpunkten för disputationen var följande delarbete opublicerat: delarbete I (manuskript).

At the time of the doctoral defence the following paper was unpublished: paper I (manuscript).

Available from: 2021-03-26 Created: 2021-03-25 Last updated: 2024-03-04Bibliographically approved

Open Access in DiVA

fulltext(1985 kB)409 downloads
File information
File name FULLTEXT01.pdfFile size 1985 kBChecksum SHA-512
6616fcd544059fe7bd6b7bcde9e1e83010a336a4ea103d3a4e7b7642d2cfb20a2367f6cef95f8c332116604c40b1f1236a47ccd0678ac6e0598064a529dd7025
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Azadvar, Ahmad

Search in DiVA

By author/editor
Azadvar, Ahmad
Human Computer Interaction

Search outside of DiVA

GoogleGoogle Scholar
Total: 412 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 228 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf