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

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.Comment: Version accepted for IEEE Conference on Games, 201

Predictive Psychological Player Profiling

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.   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).</p

Predictive Psychological Player Profiling

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.   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).</p

UPEQ: ubisoft perceived experience questionnaire : a self-determination evaluation tool for video games

In order to appeal to a growing market, game developers are offering a wide variety of activities. It is becoming necessary to understand which psychological need each activity caters for. The purpose of this paper is to demonstrate the development and evaluation of an instrument to assess which basic psychological needs are satisfied by different video games. This work is part of a growing effort in HCI to develop surveys able to capture subtle nuances of the player experience. This model, UPEQ, was developed by transforming a self-determination theory questionnaire into a video game specific survey. UPEQ consists of three subscale of Autonomy, Competence and Relatedness, which, through two studies focusing on development and validation of the model showed significant correlations with other self-reported measures of sense of transportation to the game as well as enjoyment of and engagement with the game. Regression with ingame behavior of players tracked by game engine also confirmed that each subscale of UPEQ independently predicts playtime, money spent on the game and playing as a group

Aging Agents : Cross Generational Analysis of Behavior and Need Satisfaction Among Players of Tom Clancy’s The Division 2

This research investigated the effect of age on players of an online multiplayer shooter. Through combining the data from two large scale surveys, we collected information regarding age, gaming habits, game rating and psychological need satisfaction for 8120 players of Tom Clancy’s The Division. Behavioral data extracted from the game’s tracking engine was then cross-referenced for different age groups to indicate motivational, behavioral and habitual characteristics of each age group. To find the importance of measured factors we employed a rank-based model for comparing independent sample means for intergenerational analysis (Kendall’s tau for non-parametric correlations) as well as multiple Machine Learning algorithms. Results found that different measures of playtime vary significantly among generations. Baby Boomers showed significantly higher playtime, days played and group playtime. Intergenerational comparison of perceived need satisfaction also found that older gamers feel more agentic, present in the narrative, closer to non-playable characters but less competent at the game. Percentage of group playtime also showed a decrease in older generations. Future research may expand cross generational analysis to other game types and include more granular behavioral measures

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

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