Personality-targeted Gamification

Jia, Yuan; Xu, Bin; Karanam, Yamini; Voida, Stephen

doi:10.1145/2858036.2858515

Cited by 170 publications

(77 citation statements)

References 34 publications

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“…Their work grouped personality traits, player types, and game elements into five categories: dominant, objectivist, humanist, inquisitive, and creative. Jia et al [21] studied the relation between the five-factor model (FFM) personality traits [19] and individual gamification elements and found several significant correlations. Orji et al [31] studied the relation between the FFM personality traits and several persuasive strategies used in gamification and found significant correlations.…”

Section: Related Workmentioning

confidence: 99%

“…This often involves educating users about topics in which they lack knowledge, supporting them in attitude or behaviour change, or engaging them in specific areas [10]. To make gameful systems more effective, several researchers have attempted to model user preferences in these systems, such as user typologies [40], persuasive strategies [31], or preferences for different game elements based on personality traits [21]. Nevertheless, none of these models have studied elements used specifically in gameful design.…”

Section: Introductionmentioning

confidence: 99%

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Elements of Gameful Design Emerging from User Preferences

Tondello

Mora

Nacke

2017

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

125

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Several studies have developed models to explain player preferences. These models have been developed for digital games; however, they have been frequently applied in gameful design (i.e., designing non-game applications with game elements) without empirical validation of their fit to this different context. It is not clear if users experience game elements embedded in applications similarly to how players experience them in games. Consequently, we still lack a conceptual framework of design elements built specifically for a gamification context. To fill this gap, we propose a classification of eight groups of gameful design elements produced from an exploratory factor analysis based on participants' self-reported preferences. We describe the characteristics of the users who are more likely to enjoy each group of design elements in terms of their gender, age, gamification user type, and personality t raits. Our main contribution is providing an overview of which design elements work best for what demographic clusters and how we can apply this knowledge to design effective gameful systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Elements of Gameful Design Emerging from User Preferences

Tondello

Mora

Nacke

2017

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

125

View full text Add to dashboard Cite

show abstract

“…eir work grouped personality traits, player types, and game elements in ve categories: (1) Dominant, (2) Objectivist, (3) Humanist, (4) Inquisitive, and (5) Creative. Jia et al [29] studied the relation between the ve-factor model (FFM) personality traits [15,25] and individual gami cation elements and found several signi cant correlations. Orji et al [52] studied the relation between the FFM personality traits and several persuasive strategies used in gami cation.…”

Section: Personalized Gami Cationmentioning

confidence: 99%

“…Results have shown the higher value of personalized over generic approaches in user interface (UI) design [3,44], persuasive technologies [31,33], and games [5,14,50,53]. However, the study of personalized gami cation is still in its infancy and publications so far have been mostly theoretical, for example, focusing on identifying di erent personality traits or preferences for personalization [29,52,64].…”

Section: Introductionmentioning

confidence: 99%

Recommender Systems for Personalized Gamification

Tondello

Orji

Nacke

2017

Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

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Gami cation has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve be er results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., so ware tools and technologies to recommend suggestions to users that they might enjoy). is framework contributes to understanding and building e ective persuasive and gameful applications by describing the di erent building blocks of a recommender system (users, items, and transactions) in a personalized gami cation context.

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“…work within speci c contexts and for speci c users. Motivation, for example, arises from di erent multiple causes [8] and di erently for certain users [7,17]. As a result, such three components of gami cation, i.e.…”

Section: Motivationmentioning

confidence: 99%

Data-driven gamification design

Meder

Rapp²,

Plumbaum

et al. 2017

Proceedings of the 21st International Academic Mindtrek Conference

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Gami cation has been a racted much interest, not only in the HCI community, in the last few years. However, there is still a lack of insights and theory on the relationships between game design elements, motivation, domain context and user behavior. In this workshop we want to discover the potentials of data-driven gami cation design optimization, e.g. by the application of machine learning techniques on user interaction data in a certain domain.

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Personality-targeted Gamification

Cited by 170 publications

References 34 publications

Elements of Gameful Design Emerging from User Preferences

Elements of Gameful Design Emerging from User Preferences

Recommender Systems for Personalized Gamification

Data-driven gamification design

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