=Paper=
{{Paper
|id=Vol-2089/11_Tondello
|storemode=property
|title=Towards Customizing Gameful Systems by Gameful Design Elements
|pdfUrl=https://ceur-ws.org/Vol-2089/11_Tondello.pdf
|volume=Vol-2089
|authors=Gustavo F. Tondello,Lennart E. Nacke
|dblpUrl=https://dblp.org/rec/conf/persuasive/TondelloN18
}}
==Towards Customizing Gameful Systems by Gameful Design Elements==
https://ceur-ws.org/Vol-2089/11_Tondello.pdf
Towards Customizing Gameful Systems by Gameful
Design Elements
Gustavo F. Tondello and Lennart E. Nacke
HCI Games Group, University of Waterloo, Canada
gustavo@tondello.com, lennart.nacke@acm.org
Abstract. Recently, several researchers have suggested that personalized game-
ful systems can be more effective than generic approaches. However, there is
still scarce empirical evidence that the suggested factors for personalization,
such as gender, age, user types, and personality traits, will be effective in im-
proving user engagement and performance for personalized gameful systems. In
this work-in-progress, we present a research plan for empirical evaluation of a
customizable gameful system. Upon completion of this study, we expect to pro-
vide empirical evidence that the participants’ selection of gameful design ele-
ments in a practical application will correspond to the theorized relationships
suggested by prior survey-based research, and that the system can suggest the
gameful design elements that users are more likely to enjoy. The results of this
research will provide an actionable path for gamification designers to imple-
ment personalized gameful systems and for researchers to develop recommen-
dation algorithms for gamification.
Keywords: Gamification, Gameful Design, Personalization, Customization.
1 Introduction
Gamification, the use of game design elements in non-game contexts [1], can be em-
ployed as a toolset to increase user engagement, activity, and enjoyment of digital
interactive systems. It can also be used to create applications aimed at promoting
behaviour change in domains such as health, wellness, education, training, online
communities, customer loyalty, and marketing [2–5], thus representing a form of
persuasive technology (PT). Recently, a topic that has gained attention is understand-
ing how to personalize gameful systems to each user [6–8]. This is important because
personalized interactive systems can be more effective than generic systems [8, 9].
Gameful systems are effective when they help users achieve their goals, which often
involve educating them about certain topics, supporting them in attitude or behaviour
change, or engaging them in specific topics [6]. However, publications on personal-
ized gamification so far have been mostly theoretical, for example, focusing on identi-
fying different personality traits [10, 11] or preferences for personalization [9, 12].
In work-in-progress this paper, we present a research plan for empirical evaluation
of a customizable gameful system. Our design approach includes understanding the
different user preferences based on the Hexad framework [13], then allowing users to
Copyright © 2018 held by the paper’s authors. Copying permitted for private and academic
purposes.
In: R. Orji, M. Kaptein, J. Ham, K. Oyibo, J. Nwokeji (eds.): Proceedings of the Personaliza-
tion in Persuasive Technology Workshop, Persuasive Technology 2018, Waterloo, Canada,
17-04-2018, published at http://ceur-ws.org
102 Towards Customizing Gameful Systems by Gameful Design Elements
select the gameful design elements [14] that might be most appealing to them. More-
over, a gameful system might try to identify the user’s preferences and suggest which
elements they are more likely to enjoy [15], akin to what recommender systems (RS)
[16] do in application domains such as online commerce. Therefore, this research
aims to investigate empirically if this kind of tailored gameful system is more engag-
ing to users than generic implementations.
2 Related Work
2.1 Gamification User Types
Research on gameplay motivations has shown that players have diverse personal pref-
erences regarding how and what they play [17–19]. Researchers have developed play-
er type models [17, 20, 21] or gamer motivation scales [18, 22] to capture the diverse
styles of play exhibited by different players. This information has been increasingly
used in gamification to model user behaviour and design more engaging gameful
systems. Nevertheless, none of these models have studied elements used specifically
in gameful design. Therefore, their applicability in gamification has not been support-
ed by empirical evidence yet.
To address this problem, Marczewski [23] developed the Gamification User Types
Hexad framework, based on research on human motivation, player types, and practi-
cal design experience. He also suggested different game design elements that may
support different user types [24]. The six Hexad user types are [13, 23]:
Philanthropists are motivated by purpose. They are altruistic and willing to give
without expecting a reward.
Socialisers are motivated by relatedness. They want to interact with others and
create social connections.
Achievers are motivated by competence. They seek to progress within a system by
completing tasks or prove themselves by tackling difficult challenges.
Free Spirits are motivated by autonomy, meaning freedom to express themselves
and act without external control. They like to create and explore within a system.
Players are motivated by extrinsic rewards. They will do whatever to earn a re-
ward within a system, independently of the type of the activity.
Disruptors are motivated by the triggering of change. They tend to disrupt the
system either directly or through others to force negative or positive changes.
Moreover, Tondello et al. proposed a validated survey measure [13] for scoring indi-
viduals across these user types. They also suggested that the Hexad can be used as a
model to personalize user experience (UX) in gameful systems, by showing that there
are significant correlations between the Hexad user types and user preferences for 32
design elements commonly employed in gameful design [13]. Orji et al. [25] further
supported this suggestion by also showing significant relationships between the Hex-
ad user types and the persuasiveness of different strategies commonly employed in
persuasive technologies.
Towards Customizing Gameful Systems by Gameful Design Elements 103
2.2 Personalized Gamification
Regarding models of user preferences, Ferro et al. [10] studied several models of
personality and player types, aiming to find the similarities between them as well as to
relate them to different game design elements. Their work grouped personality traits,
player types, and game elements in five player categories: ‘Dominant’, ‘Objectivist’,
‘Humanist’, ‘Inquisitive’, and ‘Creative’. Jia et al. [11] studied the relation between
the five-factor model (FFM) personality traits [26] and individual gamification ele-
ments and found several significant correlations. Orji et al. [9] studied the relation
between the FFM personality traits and several persuasive strategies used in gamifica-
tion and found significant correlations.
Gamification also draws from research in persuasive technologies to further en-
courage adoption of behaviours. Theoretical and empirical studies have suggested
different factors for persuasive technology personalization [27, 28], such as personali-
ty types [29–31], age [32], gender [32, 33], player types [34, 35], culture or nationali-
ty [36, 37], and individual susceptibility to persuasive attempts [38, 39].
Considering the topic of gameful design elements, Tondello et al. [14] proposed a
new conceptual framework for classifying them based on participants’ self-reported
preferences, with the goal of understanding user behaviour in gamification. Their
work classified gameful design elements in eight groups [14]:
Socialization: elements corresponding to some form of social interaction, includ-
ing both collaborative, competitive, and entirely social interactions.
Assistance: elements corresponding to the user receiving some sort of aid for their
progression, either from the system or from other users.
Immersion: elements related to immersion and curiosity, including elements relat-
ed with a narrative or story or with exploration and unpredictability.
Risk/Reward: elements related to challenges, gambling, and the rewards that come
from winning.
Customization: elements related to three different ways of customizing one’s own
experience: (1) customizing the user’s avatar or experience, (2) automatic person-
alization, or (3) redeeming freely chosen goods with virtual currency or points.
Progression: elements related to progression and meaning, representing the will to
be involved in meaningful goals and feeling a progression towards achieving them.
Altruism. elements corresponding to diverse ways of making a useful contribution,
either to the system or to other users, including sharing knowledge or goods, con-
tributing to improve the system, and collaborating with other users.
Incentive. elements corresponding to incentives or rewards that the user might
receive, such as badges, achievements, collectible items, and rewards.
It is also noteworthy that despite the existing literature on user preferences in gami-
fication and games, most gameful design methods do not take user preference in con-
sideration as part of their process [40, 41]. Nonetheless, Ferro [42] has recently de-
veloped Gamicards, a methodology that helps designers create gameful experiences
by selecting game elements and mechanics tailored to the users and context of the
application.
104 Towards Customizing Gameful Systems by Gameful Design Elements
On the topic of customizing or personalizing the activities and gameful design el-
ements available for the user, three promising approaches have been recently present-
ed. Khoshkangini et al. [43] described and conducted an initial evaluation of an auto-
mated challenge generator, which is able to dynamically generate personalized chal-
lenges from templates, by tailoring the goals, difficulty, and rewards according to the
user’s preferences and skills. Altmeyer et al. [44] and Lessel et al. [45] also described
and conducted an initial evaluation of a “bottom-up” gamification approach, in each
users are given choices of available gameful elements, which they can customize by
selecting their preferred elements and adjusting some parameters (such as the amount
of points rewarded by an activity). Furthermore, Tondello et al. [15] suggested devel-
oping recommendation algorithms to suggest gameful activities, gameful elements,
and persuasive strategies that each user is more likely to enjoy in a gameful system.
Finally, Böckle et al. [7] have recently presented a systematic literature review of
the existing approaches on adaptive gamification.
3 Research Plan
Based on the related work on personalized gamification, we have reason to expect
that a customized system will be more engaging for its users and might be better able
to help the user achieve higher performance in the tasks carried out within the system.
For example, Altmeyer et al. [44] and Lessel et al. [45] provided initial evidence that
letting users customize their experience—by letting them select the game elements for
their experience—can lead to better engagement and performance. However, if a
gameful system is built with enough activities and gameful design elements to be
appealing to all types of users, a problem of information overload may occur. The
user might find so many different ways of interacting with the system that it might
become difficult to choose their preferred style among them [15]. One of the existing
solutions for this problem is the use of recommender systems (RS) [16], which are
software tools and techniques that provide suggestions for particular items to a user.
A RS can help the user find items that would probably match their preferences among
the increasing amount of available information and products. Additionally, a RS relies
on people making choices based on what other people recommend.
Although recommender systems can be a solution to tailor gameful systems to each
user, the topic has been scarcely investigated until now. As an initial development of
this idea, Tondello et al. [15] proposed a general framework that describes the possi-
ble inputs for this type of RS (items, users, transactions, and contextual information),
the possible approaches to choose a recommendation algorithm, and the process out-
put (the predicted ratings for each gameful activity per user). Nonetheless, the cur-
rently available knowledge on personalized gamification limits the current frame-
work. Particularly, we still have scarce empirical evidence that the suggested models
for personalization, such as user types and taxonomies of gameful design elements,
will be effective to help users select their preferred activities within gameful systems.
To overcome this shortcomings in the literature, we will conduct an experimental
study aimed at answering the following research questions:
Towards Customizing Gameful Systems by Gameful Design Elements 105
RQ1. If allowed to choose the gameful design elements they prefer, do user choices
correspond to the theoretical relationships with user types, personality, gender,
and age reported in previous survey-based studies [13, 14]?
RQ2. Do user engagement and performance improve if the application helps them
customize their gameful experience by suggesting the gameful design elements
they are more likely to enjoy (based on the answer to RQ1)?
3.1 Study Design
We will build an online crowdsourcing platform in which participants will be asked to
complete classification and brainstorming microtasks. Each task will consist in listing
all the classification tags that the participant can think of for a stock image. Partici-
pants will create an account and will be encouraged to complete as many microtasks
as they wish during the study period. The use of classification microtasks was already
reported on previous studies of customizable gamification [44, 45]; therefore, this is
an interesting type of task to allow for comparisons with previous results. Moreover,
brainstorming tasks have also been used in previous empirical studies of gamification
[46] because they were found to be good types of tasks to investigate task perfor-
mance in relation to goal setting. Hence, we will be able to implement gameful design
elements that motivate participants in two levels: (1) to complete more microtasks and
(2) to perform better in each task by listing a higher number of tags.
The crowdsourcing platform will include gameful elements to motivate and en-
courage participants to complete more microtasks and to perform better in each task.
To allow users to customize their gameful experience, we will include two elements
from each one of the eight groups from our previous classification [14]. This will give
users a broad range of experiences to select from. From the 16 available gameful de-
sign elements, each participant will be allowed to select up to four elements to cus-
tomize their experience. This limitation is added to ensure that users will have to
spend some time selecting the elements that they prefer. The 16 gameful design ele-
ments currently planned for the system design are:
Socialization: leaderboards and social competition
Assistance: glowing choice and beginner’s luck
Immersion: Easter eggs and theme
Risk/Reward: lotteries and challenges
Customization: avatars and points
Progression: levels and progress feedback
Altruism: knowledge sharing and gifting
Incentive: badges and rewards
The study will be divided in two phases:
First Phase. The goal of the first phase is to answer RQ1. Therefore, all participants
will be allowed to choose any gameful design element from the list, without any sug-
gestion from the platform. By doing this, we will be able to verify if participants’
106 Towards Customizing Gameful Systems by Gameful Design Elements
choices will correspond to the theorized preferences by user types, personality traits,
gender, and age reported by the previous studies.
Second Phase. The goal of the second phase is to answer RQ2. Therefore, we want to
test if it is useful for the platform to suggest the gameful design elements that each
user is more likely to enjoy aiding them in their customization. For this purpose, we
will split participants into three conditions:
C1: Tailored: in this condition, the application will suggest the four gameful ele-
ments that the user will be more likely to enjoy based on their profile.
C2: Contra-tailored: this is the opposite of C1; thus, the application will suggest the
four elements that the user is less likely to enjoy.
C3: Control: in this condition, participants will not receive any suggestion from the
platform regarding their selection of gameful design elements.
Measurements. During the first phase, we will record each participant’s choice of
gameful design elements. Therefore, we will analyze if the independent variables
(gender, age, user type, and personality traits) can predict the participants’ choices for
the gameful design elements and if the relationships between them correspond to the
theorized relationships from previous works [13, 14]. In the second phase, we will
measure participants’ engagement (with measures such as the number of completed
tasks) and performance (average number of tags identified for each microtask com-
pleted). Moreover, to better understand the user experience with the customization of
their gameful system, we will also include a few additional free-text questions, which
will focus on their impressions about the activity of selecting gameful design ele-
ments and their general enjoyment of the platform.
4 Conclusion
In this work-in-progress paper, we have described our research plan for an experi-
mental study aimed at demonstrating the viability of design customizable gameful
interactive systems according to user preferences. Upon completion of this study, we
will be able to provide two main contributions to the extant literature on personalized
gameful systems. First, we expect to provide empirical evidence that the participants’
selection of gameful design elements in a practical application will correspond to the
theorized relationships suggested by prior survey-based research [13, 14]. Second, we
expect to provide empirical evidence that it is possible to implement a simple system
to help users overcome the information overload problem, by suggesting the gameful
design elements that they are more likely to enjoy based on their user types and de-
mographic information. The results of this research will provide an actionable path for
gamification designers to implement personalized gameful systems. Furthermore, the
empirical evidence that will be collected as part of this research will represent a valu-
able model, which in the future could be used to implement recommendation algo-
rithms for gameful systems [15].
Towards Customizing Gameful Systems by Gameful Design Elements 107
Acknowledgments
This research received funding from the CNPq, Brazil, the University of Waterloo,
NSERC (RGPIN-418622-2012), SSHRC (895-2011-1014, IMMERSe), CFI (35819),
and Mitacs (IT07255).
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