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Outline

Title
Abstract
Introduction
Related Work
Study I: The Player Traits Model
Participants
Study I Discussion
Methods
Free-Text Data Cleaning
Hypotheses
Results
Study II Discussion
General Discussion
Limitations
Conclusion
References
All Topics
Computer Science
Human-Computer Interaction

Towards a Trait Model of Video Game Preferences

Profile image of Rita OrjiRita Orji

2018, International Journal of Human–Computer Interaction

https://doi.org/10.1080/10447318.2018.1461765
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52 pages

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Abstract

Typologies for understanding players' preferences towards different gameplay styles have gained popularity in research. However, attempts to model players' preferences are based on type models instead of trait models, contrary to the latest personality research. One such model, BrainHex, was designed as an interim model to enable investigations towards a definitive player trait model. However, it lacks empirical validation in support of its psychometric properties. The present work analysed a dataset with over 50,000 respondents to devise a player traits model based off the BrainHex scale. Results indicate three player traits: action, esthetic, and goal orientation. Furthermore, we analysed the games listed by participants as examples of what they enjoy, to understand which factors influence player preferences. Results illustrate that the emergent player traits and participants' genders and attitudes towards story can partially explain player preferences towards certain games. Finally, we present the implications towards a definitive player traits model.

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    Julita Vassileva

    Smart Learning Environments, 2022

    Gamification refers to the attempt to transform different kinds of systems to be able to better invoke positive experiences such as the flow state. However, the ability of such intervention to invoke flow state is commonly believed to depend on several moderating factors including the user's traits. Currently, there is a dearth of research on the effect of user traits on the results of gamification. Gamer types (personality traits related to gaming styles and preferences) are considered some of the most relevant factors affecting the individual's susceptibility to gamification. Therefore, in this study we investigate how gamer types from the BrainHex taxonomy (achiever, conqueror, daredevil, mastermind, seeker, socializer and survivor) moderate the effects of personalized/non-personalized gamification on users' flow experience (challenge-skill balance, merging of action and awareness, clear goals, feedback, concentration, control, loss of self-consciousness and autotelic experience), enjoyment, perception of gamification and motivation. We conducted a mixed factorial within-subject experiment involving 121 elementary school students comparing a personalized version against a non-personalized version of a gamified education system. There were no main effects between personalization and students' flow experience, perception of gamification and motivation, and enjoyment. Our results also indicate patterns of characteristics that can lead students to the high flow experience (e.g., those who prefer to play multiplayer have a high flow experience in both personalized and non-personalized versions). Based on our results, we provided recommendations to advance the design of gamifed educational systems.

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