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Profile-Based Algorithm for Personalized Gamification in
Computer-Supported Collaborative Learning Environments
Antti Knutas
Lappeenranta University of
Technology
Lappeenranta, Finland
antti.knutas@lut.fi
Rob van Roy
KU Leuven
Leuven, Belgium
Rob.vanRoy@soc.kuleuven.be
Timo Hynninen
Lappeenranta University of
Technology
Lappeenranta, Finland
timo.hynninen@lut.fi
Marco Granato
University of Milan
Milan, Italy
marco.granato@unimi.it
Jussi Kasurinen
South-Eastern Finland
University of Applied Sciences
Kotka, Finland
jussi.kasurinen@xamk.fi
Jouni Ikonen
Lappeenranta University of
Technology
Lappeenranta, Finland
jouni.ikonen@lut.fi
ABSTRACT
In this paper we present an approach for personalizing gamifi-
cation to the needs of each individual person. We designed the
personalization for computer-supported collaborative learning
environments by synthesizing three existing design frame-
works: the lens of intrinsic skill atoms, gamification user type
hexad and heuristics for effective design of gamification. The
result of the design process is a context-aware and personal-
ized gamification ruleset for collaborative environments. We
also present a method for translating gamification rulesets
to machine-readable classifier algorithm using the CN2 rule
inducer and a framework for connecting the produced algo-
rithm to collaborative software. Lastly, we present an example
software for personalized gamification that was produced by
applying the process presented in this paper.
ACM Classification Keywords
H.1.2. User/Machine Systems: Human Factors; I.2.1. Appli-
cations and Expert Systems: Games; K.3.1. Computer Uses
in Education: Collaborative Learning
Author Keywords
gamification, adaptive systems, personalization,
computer-supported collaborative learning
INTRODUCTION
Collaborative learning is a learning method where students
have a symmetry of action, knowledge and status, and have a
low division of labor [9]. Computer-supported collaborative
learning facilitates the interaction with software tools and
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GHITALY17: 1st Workshop on Games-Human Interaction, September 18th, 2017,
Cagliari, Italy.
increases potential for creative activities and social interaction
[26]. In recent studies, it has been shown that students can
be guided towards educational goals like collaboration by
using gamification [20], which is the application of game-like
elements to non-game environments [8].
However, gamification is not a "one size fits all" solution [29].
Early research on the gamification of education concentrated
on exploratory research and proof of concepts, or specifying
the user interface elements by which gamification manifests
in systems [24]. Recent literature reviews assessing the po-
tential of gamification in education found several positive
implications, like increased engagement and motivation [20,
24], although some studies also link gamification to negative
consequences, like unproductive competition or reward satu-
ration that leads to demotivation [10, 24]. Different authors
have pointed to contextual and personal differences to explain
these mixed results and have called upon future research to
take these characteristics into account [2, 18, 29].
We propose that in order to make gamification more user-
centric and customized to the individual user in computer-
supported collaborative learning (CSCL) environments, the
systems should include profiling of users in its design princi-
ples and selectively choose gamification features presented to
each user. In this study, we present the design process for cre-
ating an algorithm to choose challenge-type gamification tasks
in CSCL systems, and a proof-of-concept algorithm. More
specifically, our research goals are:
1. How can personalized gamification features be designed to
address the preferences of different user types?
2. How could customized, profile-based gamification chal-
lenges be assigned to different users in CSCL environments?
In the design process, we use the design heuristics for effec-
tive gamification in education [23] to create a gamification
task ruleset personalized for each user type as defined in the
gamification user type hexad [28]. We then use the CN2 rule
GHITALY17: 1st Workshop on Games-Human Interaction, April 18th, 2017,
Cagliari, Italy.
Copyright © 2017 for the individual papers by the papers' authors. Copying
permitted for private and academic purposes. This volume is published and
copyrighted by its editors.
induction algorithm [4] to create a classifier to identify differ-
ent conditions that occur in a CSCL environment as discovered
by Knutas [15] and to recommend gamification tasks for the
main CSCL system.
GAMIFICATION IN EDUCATION
Approaches that use some elements of gamification have been
shown to increase student collaboration and motivation in edu-
cational settings [20]. However, effective gamification is about
using the game elements to foster users’ three innate needs for
intrinsic motivation
1
[24], originally adapted from Deci and
Ryan’s self-determination theory [5]. These principles are [5]:
Relatedness, the universal need to interact and be connected
with others; Competence, the universal need to be effective
and master a problem in a given environment; Autonomy, the
universal need to control one’s own life.
Studies in the field indicate that gamification methods are
successful in fostering collaboration, especially when follow-
ing the principles of self-determination theory [16, 27]. At
the same time, individual elements of gamification have been
studied, and recent research concludes that simply applying
a single outward aspect of gamification, like badges or other
repetitive rewards [13, 24], does not work, and instead gamifi-
cation has to consider the motivation and goals of the course
as a complete system.
PERSONALIZATION IN GAMIFICATION
Different authors have pointed to various potential confounds
while aiming to explain the mixed results found in literature.
For example, the unexpected negative effects could be due
to bad gamification design [10, 13] or to the particular inter-
play between the gamified system and the implementation
context [8, 29]. Also, personal characteristics are hypothe-
sized to impact gamification’s potential [2, 18], as such being
a possible explanation for the otherwise-presumed negative
consequences.
Research shows that different users interpret, functionalize
and evaluate the same game elements in radically different
ways [22]. In much the same way, Koster [17] reasons that
it is impossible to design a universal "fun" game, as different
predispositions and social structures bring a unique, personal-
ized sense of fun for everyone. To exemplify this personalized
meaning making of game elements, Antin and Churchill [1]
theoretically distinguished five different functions a user can
ascribe to a badge. Taken together, these results make an argu-
ment for using gamification that is specifically tailored to its
users, in order for gamification to live up to its full potential
[6, 23]. Furthermore, the success of such personalization tech-
niques has already been proven in other digital contexts, like
persuasive technologies and games (see for example [14, 25]).
In this line of reasoning, we argue that player types can be
a valuable tool to personalise gamification. This way, we
build on Monterrat and colleagues’ work [19] in which they
use predefined player types and a player adaptation model in
order to improve the matching of gamification elements to the
preferences of the user.
1Intrinsic motivation in gamification literature; autonomous mo-
tivation in self-determination theory literature
Gamification Player Type Hexad
We selected the gamification user type hexad by Tondello et al.
[28] as a model for personalized design when creating gamifi-
cation approaches. They used a survey with 133 participants
and quantitative methods first to develop and then validate a
response scale for assessing user preferences. This user model
was selected over alternatives because it is evidence-based and
gamification-specific.
The user types are summarized in Table 1. With each user
type we also present intended gamification approach. The
disruptor user type was defined as out of scope in this project.
This user type tends to disrupt the system and is difficult to
address within the context of the system. Instead, they will be
addressed by other types’ autonomy and relatedness -related
challenges and by being involved in the development of the
system.
GAMIFICATION DESIGN PROCESS
Technology designed for changing users’ attitudes or behav-
ior in online systems is known as persuasive technology [11].
Oinas-Kukkonen and Harjumaa further define persuasive soft-
ware as “computerized software or information systems de-
signed to reinforce, change or shape attitudes or behaviors
or both without using coercion or deception” [21, p. 486].
Adding gamification features to computer-supported collabo-
rative learning can be considered persuasive software because
the design intent is to change user behavior.
We used the three-element persuasion context framework de-
fined by Oinas-Kukkonen and Harjumaa [21] to initially frame
the design for the personalized gamification system. The intent
(1) is on the part of the designers is to increase collaboration.
The designers intend to use the principles of gamification first
to affect behavior that leads to positive attitude changes. The
event (2), or the use context, is user activity in the collaborative
system. Their goal is to accomplish course-related tasks. Our
strategy (3) for persuasion is to use gamification elements to
give users personalized, constructive gamification tasks and
motivating feedback through the system.
The overall design process followed Deterding’s framework
[7] for creating gameful designs. The framework presents
principles to create gameful designs for motivation and en-
joyment, which can be applied to create gamified software.
Using these principles, Deterding states that "in pursuing her
needs, a user’s activity entails certain inherent, skill-based
challenges. A gameful system supports the user’s needs by
both (a) directly facilitating their attainment, removing all ex-
traneous challenges, and (b) restructuring remaining inherent
challenges into nested, interlinked feedback loops of goals,
actions, objects, rules, and feedback that afford motivating
experiences." [7, p. 315]
Our design process followed the five steps presented in Deter-
ding’s framework [7], detailed in the following paragraphs.
1. Strategy. Target outcome is increased collaboration between
students and increased engagement in the CSCL platform.
The flexibility of the system is constrained by automatically
measured environmental variables and the functionality of the
platform.
Player type Description Provided gamification tasks
Philanthropist Motivated by purpose. They are altruistic and willing to give
without expecting a reward.
Tasks that direct help to those who need it most at the
moment. For example individuals asking for help or teams
with unsolved issues.
Socialiser Motivated by relatedness. They want to interact with others
and create social connections.
Tasks that channel the socialization impulse to upkeep the
collaborative spirit of the environment.
Free spirit Motivated by autonomy, meaning freedom to express
themselves and act without external control. They like to
create and explore within a system.
Tasks that channel exploration into sharing resources, and
tasks that acknowledge and reward the joy of discovery.
Achiever Motivated by competence. They seek to progress within a
system by completing tasks, or prove themselves by tackling
difficult challenges.
Easiest to address within the framework. Tasks that are
competitive or gather around achieving the "next level", e.g.
with points or badges.
Player
Motivated by extrinsic rewards. They will do whatever to earn
a reward within a system, independently of the type of the
activity.
Similar to achiever’s, except the mix of tasks includes more
tasks that encourage working with others and building a
positive sense of community.
Table 1. Gamification player types [28] and personalized approaches
2. Research. The user activity was translated into behavior
chains by analyzing current literature on CSCL and using prin-
ciples of persuasive design to frame the event structure. User
needs and motivations were adapted from current literature on
motivation and Tondello’s evidence-based gamification user
type hexad.
3. Synthesis. The principles of self-determination theory
[5], collaborative learning [9] and heuristics for the design of
gamification in education [23] were used to design challenges
in the form of gamification tasks presented to the users. These
were considered in the context of possible actions that can be
taken in a CSCL system.
4. Ideation was performed in a series of workshops, where a
panel of experts ideated rules with a note-taker translating the
ideas to the skill atom framework and presenting the results
for approval. The panel of experts consisted of three experts
on game design, three experts on gamification and education,
and two software engineers. The ideation process resulted in
a total of 69 gamification tasks for five different player types.
When duplicates were collated, it resulted in 42 individual
tasks.
5. Iterative prototyping, the last step, was performed partly
and left partly for future work. The ruleset and the algorithm
were tested and evaluated. Combining the ruleset with a live
CSCL system is part of future work.
Design Heuristics for Gamification
The panel of experts that participated in design workshops
were informed by principles of good collaborative learning
[9, 12], gamification user type hexad [28], and the self-
determination theory -based design heuristics for effective
gamification of education [23] during the design process of
the ruleset. Below, we present the design heuristics and how
they guided the design process.
#1 Avoid obligatory uses. The computer-supported collabo-
rative learning environment and especially its gamification
features should be voluntary to use.
#2 Provide a moderate amount of meaningful options. The
user is able to choose which gamification tasks to accomplish,
if any. Furthermore, as the challenges are based on the user’s
characteristics, these challenges are relevant to each person
and as such present meaningful options to the user.
#3 Set challenging but manageable goals. No task is meaning-
less or impossible to accomplish. Also, the difficulty level of
the implemented challenges are tuned to the users’ capabilities,
as such keeping the tasks manageable, while at the same time
being challenging.
#4 Provide positive, competence-related feedback. Just as
tasks should be meaningful, the feedback is meaningful and
positive. There should not be any feedback that can be per-
ceived as a punishment. When presented in a CSCL system,
the feedback should make the user feel capable.
#5 Facilitate social interaction. There are several gamification
tasks that show the positive impact the user’s actions can have
on each other. CSCL systems are social by their nature and
several tasks promote positive interaction.
#6 When supporting a particular psychological need, be wary
to not thwart the other needs. The gamification tasks should
not concentrate on promoting only one aspect over others. For
example, when promoting relatedness and prompting users to
interact, users should not feel that they are forced to, and thus
feel less autonomous.
#7 Align gamification with the goal of the activity in question.
Gamification tasks support both motivation and goal achieve-
ment. CSCL systems should not distract from accomplishing
actual team and learning goals.
#8 Create a need-supporting context. The system should
be voluntary, open and supportive. When the algorithm is
integrated to a CSCL environment, it should be presented as a
supportive feature, not the main feature.
#9 Make the system flexible. The gamification system is adap-
tive, providing personalized challenges to different user types.
The adaptive approach is the main novel contribution of this
project for CSCL systems.
Structuring Gamification Tasks
Deterding’s framework provides a method to structure gamifi-
cation design elements, called the lens of intrinsic skill atoms
[7]. It uses two elements, skill atoms and design lenses, to
identify challenges in a user’s goal pursuit and restructure
them to afford gameplay-characteristic motivating, enjoyable
experiences. Deterding names this design perspective the lens
of intrinsic skill atoms. Design lenses combine a memorable
name, a concise statement of a design principle and a set of
focusing questions to evaluate game design from a specific
perspective [7]. Skill atoms originate from an effort to develop
a formal grammar for games, in which skill atoms are the
smallest defined elements, of which the following are used
in gamification: goals, actions, objects, rules, feedback, chal-
lenge, and motivation.
We used this lens of intrinsic skill atoms to structure our gam-
ification system’s elements. The columns of Table 2 follow
this structure. The table presents one sample gamification task
for each player type.
Goal: An extra, quest-like challenge that the user needs to
accomplish. Something that is presented to the user by the
system based on the recommendation of the algorithm.
Action: Set of actions that the user can take in the system to
achieve the goal. Defined in columns Task 1 and 2.
Object: What the user can act upon, or the system state. In
this case the conditions of Prerequisite 1 to 3 define which
goals and actions are presented to the user.
Rules: Specification of what actions the user can take and how
they affect the system. In this system’s case they are inherent
to the functioning of the CSCL environment and the variables
monitored by the system.
Feedback: Sensory information that informs the user of system
state changes. In this system’s case this is left open for the im-
plementer of the CSCL environment. However, one minimal
approach is presenting a notification and a badge when a goal
has been achieved by a user’s actions.
Challenge: The difficulty of achieving the goal, caused by the
difference in system state and user’s perceived current skill.
The tasks should be meaningful and always make the user feel
that he or she made a real contribution to the collaborative
environment.
Motivation: The psychological needs energizing and directing
the user to seek out and engage with the system. In this sys-
tem’s case feelings of competence, relatedness, and autonomy.
ALGORITHM FOR ADAPTIVE GAMIFICATION
The algorithm is based on the ruleset presented in the pre-
vious section. It is designed to choose context-dependent,
personalized gamification tasks for users of a specific variety
of a computer-supported collaborative learning system. It is
based on a classifier created with the CN2 rule induction al-
gorithm [4], which condensed the ruleset into a set of if-else
-conditions. When activated, it uses the environmental vari-
ables to decide which quest-type task should be presented to
the user.
In this case, gamification task means tasks that correspond to a
set of goals that need to be met, in a manner that is for example
similar to a quest in a video game. The task assignment,
accomplishment and feedback process follows the "new goal
- rules - action - challenge - feedback - motivation" loop of
the lens of intrinsic skill atoms [7], as presented in the design
section.
The algorithm is designed to act as a stateless plugin for a
specific type of computer-supported collaborative learning
environment. It integrates to the CSCL system as presented
in Figure 1. It depends on the system to give it snapshots
of status variables, which it uses to recommend gamification
tasks. The system is responsible for task accomplishment
tracking, feedback, and other interaction features. However,
the ruleset is also presented in a human readable format in the
online appendix and contains some recommendations for task
presentation. The algorithm depends on the CSCL system for
system status as input, such as user gamification type, user
skill, issue tracker task activity and discussion system activity.
The full list is presented in the Online Appendix 2.
1. Interaction 4. Response and
gamification tasks
(2). User
behavior
parameters
(3). Gamification
task proposal, if
conditions match
Figure 1. System diagram of a CSCL environment and the algorithm
The algorithm design makes the following assumptions on
the system: 1) The users of the systems are students who
are willing and allowed to help each other, 2) the students
are engaged in collaborative teamwork and have series of
tasks to do, 3) there is a system to track the tasks assigned,
such as GitHub or a CSCL system presented by Knutas [15],
4) the system tracks when participants work on tasks and
allows external help, and 5) there is a free-form synchronous
discussion system associated with the CSCL environment.
Machine-Format Rule Creation with CN2
The CN2 rule induction algorithm is a basic component of
many machine learning systems. It creates a list of classifica-
tion rules from examples using entropy as its search heuristics
[4]. In this case, the examples are the list of prerequisites that
can trigger the conditions for providing personalized gamifi-
cation tasks and the classes are individual gamification tasks
the algorithm should offer. The CN2 rule inducer was origi-
nally designed to function in a noisy environment and to find
a minimal number of rules that cover a maximal number of
cases. The list of cases was already pre-vetted by the panel of
2https://doi.org/10.5281/zenodo.827225
Prerequisite 1
(player type)
Prerequisite 2 Prerequisite
3
Task 1 Task 2
Philantropist High user skill Low skill user in
chat
Write in chat
Get upvote from low skill user
Socializer Low chat activity High user skill in
chat
Carry out chat activity for 15
minutes
(none)
Free spirit
Own team has low activity
Other teams are
active
Check the status of all other
teams
Start a discussion in chat on
one found item
Achiever Point difference between
teams is low
Player team is
not on top
Raise your team to the top of
the scoreboard
(none)
Player Own team has many
unsolved issues
Own team has
old issues
Get other team to help Issue is solved
Table 2. Sample gamification tasks for each player type and their triggering conditions
experts, so the CN2 inducer parameters were deliberately set
to cause overfitting in order to cover all of the cases.
The rule induction process from 69 human-defined rules re-
sulted to 59 machine format if-else rules. For example, the
rules for the third task (Free spirit) in Table 2 was induced
into a following rule: "IF Hexad = Free Spirit AND Chat
Activity != Low AND Ownteam opentasks = high AND Own-
team task age = high AND Ownteamactivity != high THEN
Challenge_class = 7 (Quality 0.125)". The CN2 rule inducer
was used in unordered mode, which means all the rules are
evaluated and the algorithm does not stop after the first match.
When several rules match, the one with the highest quality is
selected.
The full list of rules, training data, variables and the algorithm
itself, stored as an Python-based Orange Data Mining classifier
3
, are available in the Online Appendix
2
. Orange was selected
as the classifier implementation because it provides a Python-
based library and enables programmers to load and use the
classifier without in-depth knowledge of machine learning.
The appendix contains a short, interactive program for testing
the classifier.
DISCUSSION AND CONCLUSION
All gamification approaches are not suited for everybody,
which means that for gamification to have more of an impact,
the gamification system should be personalized to respond to
the needs of each individual user. In this paper we presented
an approach to create personalized gamification rulesets us-
ing a framework [7] for creating playful designs and design
heuristics [23] for effective gamification (research goal 1). The
ruleset was induced into machine-format rules that can be used
as a plugin algorithm for computer-supported collaborative
learning environments in order to select personalized gami-
fication tasks for specific user types and situations (research
goal 2).
The presented algorithm can be used to improve collaborative
learning systems that are looking to add or improve personal-
ized gamification features. Moreover, it makes a distinction
between the interaction environment and interaction rules. The
decoupling between the environment and the ruleset allows
gradual development and improvement of gamification with-
out having to re-develop the logic of the entire system. It also
allows sharing rulesets as plugins for others to use.
3https://orange.biolab.si
Previous studies indicate that personal characteristics affect
how people respond to game elements [22] and this can have
an impact on the effectiveness of gamification [2, 18]. The
approach to designing user type specific rules presented in this
paper are one solution to increasing personalization in gamifi-
cation. While models and designs have been published (e.g.
[3, 19]) for personalization through adaptation in gamification,
to our knowledge this is the first published realization of such
designs in collaborative systems for education.
The approach presented in this paper builds on theoretical work
from the field of gamification research, and existing concepts
from other domains. As future work more testing with the
algorithm will be conducted by implementing a CSCL system,
which can be used to evaluate and validate the algorithm in a
series of tests.
ACKNOWLEDGMENTS
Research was partially funded by European Union Regional
Development Fund grant number A70554, "Kyberturvallisuu-
sosaamisen ja liiketoiminnan kehittäminen," administrated by
the Council of Kymenlaakso.
REFERENCES
1. Judd Antin and Elizabeth F. Churchill. 2011. Badges in
social media: A social psychological perspective. In CHI
2011 Gamification Workshop Proceedings. ACM New
York, NY, 1–4.
2.
Gabriel Barata, Sandra Gama, Joaquim Jorge, and Daniel
Gonçalves. 2015. Gamification for smarter learning: tales
from the trenches. Smart Learning Environments 2, 1
(Dec. 2015).
3.
Martin Böckle, Jasminko Novak, and Markus Bick. 2017.
TOWARDS ADAPTIVE GAMIFICATION: A
SYNTHESIS OF CURRENT DEVELOPMENTS. In
Proceedings of the 25th European Conference on
Information Systems (ECIS). Guimarães, Portugal.
4. Peter Clark and Robin Boswell. 1991. Rule induction
with CN2: Some recent improvements. In European
Working Session on Learning. Springer, 151–163.
5.
Edward L. Deci and Richard M. Ryan. 2012. Motivation,
personality, and development within embedded social
contexts: An overview of self-determination theory. The
Oxford handbook of human motivation (2012), 85–107.
6. Sebastian Deterding. 2014. Eudaimonic Design, or: Six
Invitations to Rethink Gamification. SSRN Scholarly
Paper ID 2466374. Social Science Research Network,
Rochester, NY.
7. Sebastian Deterding. 2015. The Lens of Intrinsic Skill
Atoms: A Method for Gameful Design.
Human–Computer Interaction 30, 3-4 (May 2015),
294–335.
8. Sebastian Deterding, Dan Dixon, Rilla Khaled, and
Lennart Nacke. 2011. From Game Design Elements to
Gamefulness: Defining "Gamification". In Proceedings of
the 15th International Academic MindTrek Conference:
Envisioning Future Media Environments (MindTrek ’11).
ACM, New York, NY, USA, 9–15.
9. Pierre Dillenbourg. 1999. What do you mean by
collaborative learning? Collaborative-learning:
Cognitive and computational approaches 1 (1999), 1–15.
10. Adrián Domínguez, Joseba Saenz-de Navarrete, Luis de
Marcos, Luis Fernández-Sanz, Carmen Pagés, and
José-Javier Martínez-Herráiz. 2013. Gamifying learning
experiences: Practical implications and outcomes.
Computers & Education 63 (April 2013), 380–392.
11. Brian J. Fogg. 2002. Persuasive technology: using
computers to change what we think and do. Ubiquity
2002, December (2002), 5.
12. David W. Johnson and Roger T. Johnson. 1999. Making
cooperative learning work. Theory Into Practice 38, 2
(1999), 67–73.
13. Karl M. Kapp. 2012. The gamification of learning and
instruction: game-based methods and strategies for
training and education. John Wiley & Sons.
14. Maurits Kaptein, Panos Markopoulos, Boris de Ruyter,
and Emile Aarts. 2015. Personalizing persuasive
technologies: Explicit and implicit personalization using
persuasion profiles. International Journal of
Human-Computer Studies 77 (May 2015), 38–51.
15.
Antti Knutas. 2016. Increasing Beneficial Interactions in
a Computer-Supported Collaborative Environment.
Number 718 in Acta Universitatis Lappeenrantaensis.
Lappeenranta University of Technology.
16. Antti Knutas, Jouni Ikonen, Uolevi Nikula, and Jari
Porras. 2014. Increasing Collaborative Communications
in a Programming Course with Gamification: A Case
Study. In Proceedings of the 15th International
Conference on Computer Systems and Technologies.
ACM, 370–377.
17. Raph Koster. 2013. Theory of fun for game design."
O’Reilly Media, Inc.".
18.
Elisa D. Mekler, Florian Brühlmann, Alexandre N. Tuch,
and Klaus Opwis. 2017. Towards understanding the
effects of individual gamification elements on intrinsic
motivation and performance. Computers in Human
Behavior 71 (June 2017), 525–534.
19.
Baptiste Monterrat, Michel Desmarais, Élise Lavoué, and
Sébastien George. 2015. A Player Model for Adaptive
Gamification in Learning Environments. In Artificial
Intelligence in Education. Springer, Cham, 297–306.
20. Fiona Fui-Hoon Nah, Qing Zeng, Venkata Rajasekhar
Telaprolu, Abhishek Padmanabhuni Ayyappa, and Brenda
Eschenbrenner. 2014. Gamification of education: a
review of literature. In International Conference on HCI
in Business. Springer, 401–409.
21. Harri Oinas-Kukkonen and Marja Harjumaa. 2009.
Persuasive systems design: Key issues, process model,
and system features. Communications of the Association
for Information Systems 24, 1 (2009), 28.
22. Rita Orji, Regan L. Mandryk, Julita Vassileva, and
Kathrin M. Gerling. 2013. Tailoring persuasive health
games to gamer type. In Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems.
ACM, 2467–2476.
23.
Rob van Roy and Bieke Zaman. 2017. Why Gamification
Fails in Education and How to Make It Successful:
Introducing Nine Gamification Heuristics Based on
Self-Determination Theory. In Serious Games and
Edutainment Applications, Minhua Ma and Andreas
Oikonomou (Eds.). Springer International Publishing,
485–509.
24.
Katie Seaborn and Deborah I. Fels. 2015. Gamification in
theory and action: A survey. International Journal of
Human-Computer Studies 74 (Feb. 2015), 14–31.
25. Hayeon Song, Jihyun Kim, Kelly E. Tenzek, and
Kwan Min Lee. 2013. The effects of competition and
competitiveness upon intrinsic motivation in exergames.
Computers in Human Behavior 29, 4 (July 2013),
1702–1708.
26. Gerry Stahl, Timothy Koschmann, and Dan Suthers.
2006. Computer-supported collaborative learning: An
historical perspective. Cambridge handbook of the
learning sciences 2006 (2006), 409–426.
27. C. Thomas and K. Berkling. 2013. Redesign of a
gamified Software Engineering course. In 2013
International Conference on Interactive Collaborative
Learning (ICL). 778–786.
28. Gustavo F. Tondello, Rina R. Wehbe, Lisa Diamond,
Marc Busch, Andrzej Marczewski, and Lennart E. Nacke.
2016. The Gamification User Types Hexad Scale. In
Proceedings of the 2016 Annual Symposium on
Computer-Human Interaction in Play (CHI PLAY ’16).
ACM, New York, NY, USA, 229–243.
29. Rob van Roy and Bieke Zaman. 2015. The inclusion or
exclusion of teaching staff in a gamified system: an
example of the need to personalize. In CHI Play ’15
Workshop ‘Personalization in Serious and Persuasive
Games and Gamified Interactions’.
