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Everything in life has a bright and a dark side; and gamification is not an exception. Although there is an increasing number of publications discussing the benefits of gamification in learning environments, i.e. looking into the bright side of it, several issues can hinder learning because of gamification. Nevertheless, it seems that only few researchers are discussing the dark side of using gamification in learning environments and how to overcome it. Thus, in this paper, we discuss some of the problems of gamification, namely, addiction, undesired competition, and off-task behavior. Furthermore, to deal with both bright and dark sides of gamification at the same time, we propose a framework for intelligent gamification (FIG) that can offer the necessary infrastructure for ITS to personalize the use of gamification by monitoring risk behavior, exploring how best use game design elements to avoid their overuse and finally supporting “fading” mechanisms that gradually reduces the use of gamification and help students to concentrate on learning and not only on extrinsic motivators.
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Andrade, F. R. H., Mizoguchi, R., Isotani, S. (2016) The Bright and Dark Sides of Gamification.
Proceedings of the International Conference on Intelligent Tutoring Systems. Lecture Notes in
Computer Science v.9684, pp. 111.
The Bright and Dark Sides of Gamification
Fernando R. H. Andrade¹, Riichiro Mizoguchi², Seiji Isotani¹
¹University of São Paulo, ICMC, São Carlos, SP, Brazil¹
Japan Advanced Institute of Science and Technology, Ishikawa, Japan ²
Abstract. Everything in life has a bright and a dark side; and gamification is
not an exception. Although there is an increasing number of publications discuss-
ing the benefits of gamification in learning environments, i.e. looking into the
bright side of it, several issues can hinder learning because of gamification. Nev-
ertheless, it seems that only few researchers are discussing the dark side of using
gamification in learning environments and how to overcome it. Thus, in this pa-
per, we discuss some of the problems of gamification, namely, addiction, unde-
sired competition, and off-task behavior. Furthermore, to deal with both bright
and dark sides of gamification at the same time, we propose a framework for
intelligent gamification (FIG) that can offer the necessary infrastructure for ITS
to personalize the use of gamification by monitoring risk behavior, exploring how
best use game design elements to avoid their overuse and finally supporting “fad-
ing” mechanisms that gradually reduces the use of gamification and help students
to concentrate on learning and not only on extrinsic motivators.
Keywords: Gamification; Intelligent Tutoring Systems; Addiction; Framework.
1 Introduction
Everything has a bright side and a dark side like a coin, which has a head and a tail,
and Gamification is not an exception. Usually when people find a good thing, they tend
to focus only on its bright side. However, they should always be aware of its dark side,
to use it appropriately.
In the past few years, Gamification has been drawing attention from different areas,
with the promise of increasing users’ engagement, motivation, and promoting changes
in behavior [8]. By introducing mechanics and elements from games, several compa-
nies and research groups have been trying to increase learners’ performance, commu-
nication between different groups of people, and promote better health care and healthy
habits [1]. Specifically, in the educational field, several studies have been studying dif-
ferent techniques and benefits of using gamification to raise students’ engagement level
and reach the flow state with significant findings [24].
Although several positive effects of using gamification has been found to date, par-
ticularly to improve student’s performance and increase engagement [8], researchers
and educators are ambivalent about using game like materials in education since they
could cause addiction and increase the externalization of behaviors that can hinder
learning [5, 6].
This fear should be taken seriously since many recent empirical research reports the
benefits of gamification as unexpected side effects, and not as a result of a well-thought-
out design [1, 3, 4]. It shows that the gamification implementation techniques are still
unconsolidated. Yet, according to two literature reviews on the topic, there are no stud-
ies addressing the potential negative effects of gamification in Intelligent Tutoring Sys-
tem (ITS) or any other kind of Virtual Learning Environment (VLE) [1, 7].
Thus, the main goal of this work is to discuss the potential harms of using game
elements in an ITS and propose a general framework to use gamification in an intelli-
gent way. Considering positive and negative aspects and suggesting ways to fade the
gamification elements to cope with addiction/dependence on gamification.
The remainder of this paper is structured as follows: Section 2 describes the related
works. Section 3 discusses the dark side of gamification and the proposed framework.
Section 4 presents our envisioning application of the framework and how to use it. Sec-
tion 5 concludes the paper with our final thoughts and the directions towards the vali-
dation of our Framework for Intelligent Gamification (FIG).
2 Background: gamification, flow and addiction
Kapp [8] defines gamification as Using game-based mechanics, aesthetics and
game thinking to engage people, motivate action, promote learning, and solve prob-
lems. The definition of the concept changes slightly according to different authors, but
the core idea remains the same, that is, gamification as a tool to “increase engagement
in some activity using game features, providing enjoyment and fun[13, 9, 10].
The motivational background of gamification usually relies on the SDT (Self Deter-
mination Theory) [11], which considers that a human being has three basic needs: com-
petence, relatedness, and autonomy. Based on the degree of a person’s needs and the
kind of activity, he/she can be more or less motivated to perform some activity. Ac-
cording to this theory, the user levels of motivation [11], vary from amotivated (without
any motivation to perform the activity) to intrinsically motivated (when the user doesn’t
need any external incentive to perform it). Thus, the gamification theory proposes that
by introducing game elements in an environment to satisfy some of the user’s needs, it
is possible to make the activities more attractive, even if he/she is not intrinsically mo-
The most common game mechanic applied in educational environments is the re-
ward system based on fast feedback about the students’ performance in the form of
points, trophies and badges and the division of the domain content in small units repre-
senting game levels [12, 13]. Furthermore, the use of leaderboards is also a common
tool to stimulate competition [1, 14].
One of the main goals of using gamification is to keep users in flow. The flow is a
state of deep concentration in which the user becomes so engaged in the task that he/she
loses self-awareness, and track of time[15]. Also known as optimum experience flow;
a highly desired state by game developers, considering that they want to keep the player
entertained and engaged as much as possible.
The idea of using gamification in learning environments to put students in a flow
state while they are learning is quite attractive to be implemented [2, 16]. On the other
hand, a number of studies has been conducted addressing the flow state as a factor
associated to game addiction. For example, Sun[17] conducted a research with 234 us-
ers, in which they found evidences that associate addiction in mobile games with per-
ceived visibility and flow. Perceived visibility is related to the notion of being noticed
by peers and in a position of social presence. Gamification designers also seek to in-
corporate this characteristic in the systems, by using leaderboards and sharing user
achievements, thus fulfilling the relatedness needs of the students according to the SDT.
In another study, Jeong and Lee [6] examined whether Big Five personality traits can
affect game addiction according to psychological, social, and demographic factors. To
do so, the researchers used data from a survey of 789 game users in Korea, seeking
associations and the results showed that the neuroticism trait apparently increases game
addiction. They also observed that a general self-efficacy affected game addiction in a
negative way, whereas game self-efficacy increased the degree of game addiction. Be-
sides that, loneliness enhanced game addiction, while depression showed a negative
effect on the addiction. In the context of education, these findings could mean that a
student who is confident in his abilities to perform the task is less prone to addiction
than a student without confidence, and if the student only has confidence in his game
skills, he is more susceptible to addiction.
3 The Dark Side of Gamification
The gamification approach originates in the industry with a strong appeal from mar-
keting and service [9]. In the context of learning, to increase students engagement re-
searchers and professionals have been trying to bring flow experience and immersion
to VLE. Even though improving learners’ engagement using game elements is a highly
attractive idea, contrary to the marketing perspective, the goal is not to make the student
loyal to the system, but rather increase his learning.
Therefore, we believe that gamification can be good, as long as it is controlled and
monitored. If such measures are not taken, then this could adversely affect the effec-
tiveness of the system and hinder learning. In the following paragraphs, we will present
three problems that may appear by adding game elements and mechanics without care-
ful considerations:
Off-task Behavior: If the gamification system is untied to the educational outcomes,
the game features can be a distraction to the user. In this case, even if the user likes to
use the system, he will not learn more from it. For example, the introduction of re-
sources that provides relatedness to users, such as chats and forums. These resources
are not directly related to the learning experience, allowing to the student to spend time
in the system without focusing on learning. Another example are the customization
features, those are a very important to promote immersion, but also, allows spend time
in the system without learning.
Undesired competition: Leaderboards are a common resource to promote competi-
tion, and sense of competence. Still, it can be harmful for students with low perfor-
mance and low self-efficacy, since they can feel forced in a competition with their
peers, which can negatively affect their sense of competence and result in the reduction
of their interest and engagement.
Addiction and Dependence: Based on the literature[6, 17, 18], some game features
and sensations like flow can be regarded as addictive factors. Thus, addiction could be
a potential problem in gamified environments. Unlike the behavior of alcoholics or
gambling addicts, addiction in such environments should not have greater effects such
as loss of personal property or family disruption. However, our concern is the kind of
dependency created by the game-like experience in education, as the students can re-
source to “game the system” in order to get rewards or they may not be able to learn
without gamification features.
In the first scenario, the student could change the focus from learning the subject to
other aspects provided by the system gamification. For instance, earning points to get
a higher position in a leaderboard or unlock one exclusive or rare content in the system
and gain visibility with his peers. Typically, high positions in ranks or acquisition of
virtual goods in a gamified application depends on the progress of the system main
objective, but it is not uncommon for students to seek alternative strategies to get their
desired results [19]. In the second case, the student creates a dependence of game ele-
ments to stay engaged in the system. In other words, the student is only capable to focus
on the system and acquire some knowledge if it has game elements or some kind of
extrinsic reward for his effort. To identify this condition, the system demands infor-
mation about the relationship of the student with the game elements.
Since the evolution in the gamification in a well-designed system is highly correlated
to the success and the learning outcomes, the gamification overuse may go unnoticed;
therefore, a constant monitoring of the interactions between the user, the system and
the gamification features is required.
4 Framework for Intelligent Gamification (FIG)
There are few initiatives towards gamification taken by academics aiming at the im-
provement and the consolidation of gamification. Previous works on gamification have
proposed frameworks with different perspectives, but to our knowledge none of these
have discussed how to deal with the negative implications of gamification [20, 21].
However, as discussed before it is crucial to deal with both sides of gamification, not
only using its potential to increase the engagement, but also controlling this use of gam-
ification to avoid the creation of new problems.
In order to address this, we propose a framework based on the ITS architecture that
considers the information required to implement gamification with personalization and
can process its impacts on the students and potential harms. Further, we propose a strat-
egy to reduce the participation of overused elements by fading. Thereby, our framework
proposes to increase the engagement aligning the gamification strategies to gamer pro-
files and also to identify and handle misuses resultant from the gamification in learning
environments, which, for the best of our knowledge, was not addressed by neither the
academic community nor the industry. In Figure 1, we present the proposed framework
and its components, which are explained in the following subsections.
Figure. 1. Framework for Intelligent Gamification.
Gamification Layer.
In this work, we are not approaching the domain content gamification, in this sense
the gamification in this framework is a layer independent of the pedagogical objectives
proposed by the tutor, allowing dynamical customization. Once it interacts with the
student in order to satisfy the motivational needs of competence, relatedness and au-
tonomy, but do not change the pedagogical objectives proposed by the learning de-
signer. Currently, most of the studies only use static elements without or with at least
few personalization options, however, the game design literature and also the results of
empirical studies provide evidences indicating the need to consider user individual pref-
erences [1, 10].
Data Modules.
a) Gamification model. A game element can be considered as a game component,
it will behave according to the game mechanic attached to it, and will interact with
the user when a game event is triggered due an action taken by him [2]. The gam-
ification model contains all the possible game events that can be triggered in the
system and that are controlled and regulated by the Controller Component.
b) Student Model. The main goal of gamification is to affect the students’ motiva-
tion and behavior. In order to do so by using an intelligent approach, it is necessary
to hold enough information in the Student Model. Thus, we propose a student
model divided in five small groups of attributes, as presented in Figure. 2 and
explained in the subsequent item.
b.1) Knowledge attributes. This group contains the traditional information of the
Student Model in terms of domain knowledge or skills they learn. There are sev-
eral ways of representing students data regarding the information used by the ITS
Tutor Module to make decisions in order to provide a better quality of content and
hints. Thus, it is not in the scope of this study to address the way of representing
these data. However, it is important to clarify that there is indeed a need for data
on the student’s performance, so the knowledge base should be able to provide
these data to considerations about improvement or decreasing of student perfor-
b.2) Psychological attributes. It contains information about the student’s person-
ality traits and data on mood. As said in the previous sections, several studies
shown that the personality traits influence learning and addiction behaviors, in this
sense, the information about the students’ personality trait is a useful tool to pro-
vide evidences of an undesirable condition.
b.3) General behavior attributes. They are responsible for storing information
about the student’s habits not related to learning. Game addiction shares several
symptoms and characteristics with different kinds of addictions, so it is necessary
to expand the knowledge about the user in order to obtain evidences of a problem
cause-effect relation.
b.4) Interactions patterns attributes. The system logs record the session length,
dates, time between tasks, estimated required time to finish that tasks and the in-
formation about the interaction with the game elements. Therefore, the interaction
patterns attributes contain the analyzes of those information such as mean of in-
teractions during sections, number of tasks performed by section, mean time to
solve tasks, frequent subjects, total amount of logins, mean length of the sessions.
b.5) Gamer profile attributes. In this framework, we are considering that students
may have different gaming habits and preferences in order to provide a suitable
set of game elements and mechanics.
Figure. 2. Student Model.
c) Interaction Patterns. The interaction patterns contain the representation of an ex-
pected behavior in the system. This model represents the observable data such as
time to finish contents, number of interactions, and frequency of system use. The
interaction patterns also contain the model of expected interactions with the game
elements. This model will vary according to the gamer profile approach and the
gamification model, since it has to represent the regular interaction pattern for a
student in the case of static gamification model, or the standards for a group of stu-
dents in the case of a gamification model based on different profiles.
d) Psychological Patterns. The psychological patterns represent the information that,
when matched with the situation of one student, provide evidence that this student
may be in a risk group. It can be represented by a set of rules, preset by experts or
by a series of factors that can be used by the Reasoner to inference about the student
Operational Modules:
a) Assessment Component. The assessment component is responsible for collecting
the student’s observable and interactive data and update student model
b) Behavior Reasoner. The Behavior Reasoner is the component responsible for an-
alyzing the student’s data in order to identify risk behavior. To perform this task,
the component compares the information contained in the Student Model with the
standards model in the Interaction Patterns and Psychological Patterns. When it
identifies anomalies in the student behavior, the Reasoner may inform a human sys-
tem administrator, such as a teacher, to take an action or, as we propose in this paper,
to inform the situation to the Controller, triggering changes in the gamification
c) Controller Component. The Controller is the component is responsible for the set-
tings of the gamification layer, and in order to do so, the controller needs to cross
the information contained in the student model, gamification model, and behavior
Reasoner component. In a customizable approach, the student would be able to in-
teract with the controller, changing the suggested gamification components or pa-
rameters and, at the same time, giving information to the controller, which will
change the student’s gamer profile attributes, if needed. When the Reasoner identi-
fies that a user needs to change his interactions with some elements, the controller
may act changing the value attributed to that element in order to fade this element
for the user interest. Our definition of Fade represents the change in the attributes
of the element in order to make it less attractive or difficult to access, like changing
its colors or moving it to an area that receives less attention.
5 Envisioned Application
5.1 Information Gathering
Gamer profile: To model the gamer profile, there are several player types in game
design literature and some new types are proposed considering gamification appli-
cations [22, 23]. The game components in the system have to be consistent with the
player/user types in the chosen typology. The gamer profile is composed of player
type attributes and the values for each of these attributes are updated by the controller
according to the interaction patterns to personalize the gamification and fading for
that specific player.
Psychological Attributes: Two very common tools for data acquisition about per-
sonality traits are the Big Five [24] and the MBTI (Mayers Briggs Types Indicator)
[25]. However, several researches criticized the use of MBTI as a psychometric in-
strument. Our model is composed of the personality traits, and can contain other
psychological variables that may be used to identify anomalies in the user behavior.
For instance, history of mood changes and history of emotions.
General Behavior Attributes: The function of this model is to store complementary
information about user habits. To this effect, the use of intelligent agents or chatbots
is highly recommended. Such agents can also be used to acquire information about
mood modifications and other behavioral attributes.
Gamification Model: Each gamer profile has a list of adaptation attributes that cor-
respond to the game components that will be available to that specific profile in the
interface. Each attribute can receive a value between 0 (inactive) and 1 (fully active).
The Gamification Model contains the standards for these values, and changing these
attributes affects the standards for the player types.
Interaction patterns: Normal user behavior can be established by experts, pilot run-
ning of the system or by the behavior of the majority of the users in the system.
Psychological Pattern: The psychological pattern represents the risk group in the
system. In this sense, this model has to contemplate the traits, and the associations
with other variables that provide evidences of a risk scenario. E.g. One student that
has the trait of irresponsibility, but solves a number of tasks above the mean of the
other students, in a much shorter time than the required, should be considered as a
candidate for change.
5.2 Operation
Initially the student provides information about his gamer profile and personality
traits. After that, the Controller consults the gamification model and adapts the interface
to the elements recommended for the student. Then, the assessment component starts
to log the user’s interactions and the intelligent agent interacts with the student in pre-
determined intervals to fill the general behavior model. Once the general behavior
model is populated, the Reasoner starts to compare the patterns periodically, in order
to identify anomalies.
As the Reasoner becomes more knowledgeable about the anomalies in the student
interaction patterns, it generates a list of gamification artifacts
eligible for fading. To
maximize the learners growth capabilities, the fading method has been previously used
to minimize user’s reliance on the system’s help [26]. When an artifact hits the prede-
termined threshold, the Reasoner marks it for the fading process. Once the process
starts, the system agent makes an intervention signalizing the excess of interactions
with that artifact and tracks the user performance and interactions seeking changes in
his behavior. This intervention intends to increase his self-awareness and provides the
opportunity for self-regulation. However, if after a certain period the behavior remains
same, the system starts to fade away the artifact, up to removal, until the number of
interactions go back to normal. After that, the artifact is restored to the original state
and the agent informs the student to observe his behavior.
To identify the implications of fading on the user performance and how much he
depends of gamification to keep motivated, the student is constantly monitored. If dur-
ing the fading process the student’s performance declines, the agent makes an interven-
tion in order to find out whether this is due to fading the artifact. If the reason for the
A Gamification artifact is defined as a composition of a visual game element, that directly
interacts with the user, and the game mechanic, that define how this element will behave.
decline is inherent to the process, it provides evidence with respect to the student’s
dependence on gamification. Nevertheless, in both cases, the element is restored to the
original state and the agent informs the user about the importance of keeping focus on
learning. The artifact is restored so as not to impair their learning. Furthermore, the
intervention will reinforce his self-awareness and provide, once more, the opportunity
for self-regulation, which we believe could be more meaningful since the user knows
that he can be "punished" somehow for his overuse.
6 Concluding Remarks
Most of the time people tend to focus too much on the bright side and overlook the
dark side of matters. Similarly, the interest in gamification has been growing; however,
no one seems to have shown interest in its dark side (negative effects). In this paper,
we identified addiction as the dark side of gamification and addressed the elements used
in gamification that related to this phenomenon and how it occurs in gamified environ-
ments. Further, we proposed a framework to monitor and fade with the gamification
elements to avoid the negative implications of addiction.
Our next steps include providing a detailed addiction model for learning environ-
ments and the experimental evaluation of the fading strategy of gamification elements
and the impact of this strategy in terms of engagement and performance.
The ITS architecture was chosen because such systems consider student information
to make decisions in order to improve learning. However, we believe that the same
reasoning can be applied to any VLE with proper dynamics to interact and retain
enough information about the student and the environment.
Acknowledgments. We thank CNPq and CAPES for supporting this research.
1. Hamari, J., Koivisto, J., Sarsa, H. (2014) Does Gamification Work? A Literature Review
of Empirical Studies on Gamification. In. Proc. Hawaii Int. Conf. on System Science, pp
2. Challco, G. C., Andrade, F. R. H., Oliveira, T., Isotani, S. (2015) Towards an Ontological
Model to Apply Gamification as Persuasive Technology in Collaborative Learning
Scenarios. In: Proc. of the Simpósio Brasileiro de Informática na Educação, pp. 499-508.
3. Pedro L. Z., Lopes, A. M. Z., Prates, B. G., Vassileva, J., Isotani, S. (2015) Does
Gamification Work for Boys and Girls? An Exploratory Study with a Virtual Learning
Environment. Prof. of the ACM Symposium on Applied Computing, pp. 214-219.
4. De-Marcos, L., Domínguez, A., Saenz-de-Navarrete, J., Pagés, C. (2014) An Empirical
Study Comparing Gamification and Social Networking on e-learning. Computers &
Education 75, 8291.
5. Schmitt, Z. L., Livingston, M. G. (2015) Video Game Addiction and College Performance
Among Males: Results from a 1 Year Longitudinal Study. CyberPsychology, Behavior &
Social Networking 18, 2529.
6. Jeong, E. J., Lee H. R. (2015) Addictive Use Due to Personality : Focused on Big Five
Personality Traits and Game Addiction. International Journal of Social, Behavioral,
Educational, Economic, Business and Industrial Engineering 9(6),19951999.
7. Borges, S., Reis, H. M., Durelli, V., Isotani, S. (2014) A Systematic Mapping on
Gamification Applied to Education. In: Proc. of the ACM Symposium on Applied
Computing, pp. 216-222.
8. Kapp, K. M. (2012) The Gamification of Learning and Instruction: Game-Based Methods
and Strategies for Training and Education. Pfeiffer, San Francisco, CA
9. Huotari, K., Hamari, J. (2012) Defining Gamification - A Service Marketing Perspective.
In: Proc. of the International Academic MindTrek Conference. pp 1722.
10. Monterrat, B., Lavoué, É., George, S. (2014) A Framework to Adapt Gamification in
Learning Environments. Proc. of the European Conf. on Technology Enhanced Learning,
pp. 578-579.
11. Ryan, R., Deci, E. (2000) Intrinsic and Extrinsic Motivations: Classic Definitions and New
Directions. Contemporary Educational Psychology 25, 5467.
12. Aparicio, A. F., Vela, F. L. G., Sánchez, J. L. G., Montes, J. L. I (2012) Analysis and
Application of Gamification. In: Int. Conf. on Interacción Persona-Ordenador. 12.
13. Maragos, K., Grigoriadou, M. (2005) Towards the Design of Intelligent Educational
Gaming Systems. Proceedings of the AIED workshops, 3538.
14. Nah, F.F.-H., Eschenbrenner, B., DeWester, D., Park, S. R. (2010) Impact of Flow and
Brand Equity in 3D Virtual Worlds. Journal of Database Management 21, 6989.
15. Csikszentmihalyi, M. (2008) Flow: The Psychology of Optimal Experience. Harper
Perennial Modern Classics, New York.
16. Challco, G. C., Andrade, F. R. H., Borges, S. S., Bittencourt, I. I., Isotani, S. (2016) Toward
a Unified Modeling of Learner?s Growth Process and Flow Theory. Educational
Technology & Society 19(2), 1-14.
17. Sun, Y. Y., Zhao, Y., Jia, S., Zheng, D. (2015) Understanding the Antecedents of Mobile
Game Addiction: The Roles of Perceived Visibility, Perceived Enjoyment and Flow. In:
Proc. of the Pacific Asia Conference on Information Systems, 141.
18. Chou, T., Ting, C. (2004) The Role of Flow Experience in Cyber-Game Addiction.
CyberPsychology & Behavior 6(6), 663-675.
19. Baker, R. S. J., Walonoski, J., Heffernan, N., et al. (2008) Why Students Engage in
“Gaming the System” Behavior in Interactive Learning Environments. Journal of
Interactive Learning Research 19(2),185224.
20. Wongso, O., Rosmansyah, Y., Bandung, Y. (2014) Gamification Framework Model, Based
on Social Engagement in e-learning 2.0. In: Int. Conf. on Technology, Informatics,
Management, Engineering, and Environment, pp 1014.
21. Simões, J., Redondo, R. D., Vilas A. F. (2013) A Social Gamification Framework for a K-
6 Learning Platform. Computers in Human Behavior 29, 345353.
22. Yee, N .(2006) Motivations for play in online games. CyberPsych & Behavior 9, 772775.
23. Nacke, L. E., Bateman, C., Mandryk, R. L. (2011) BrainHex: Preliminary Results from a
Neurobiological Gamer Typology Survey. In: Proc. Int. Conf. on Entertainment
Computing, pp 288293.
24. Barrick M. R., Mount, M.K. (1991) The Big Five Personality Dimensions and Job
Performance: A Meta-Analysis. Personnel Psychology 44(1),126.
25. Myers, I. B., McCaulley, M. H., Quenk, N. L., Hammer, A. L. (1998) MBTI Manual: A
Guide to the Development and Use of the Myers-Briggs Type Indicator. Consulting
Psycologists Press, 3rd Edition., Palo Alto, CA.
26. Ueno, M., Miyasawa, Y. (2015) Probability Based Scaffolding System with Fading. In:
Proc. of the Int. Conf. on Artificial Intelligence in Education, pp. 492503.
... The elements of the Octalysis model that are in the right part represent are related to intrinsic motivation, as opposed to the elements on the left side, which relate to extrinsic motivation (Bernik, 2021). The elements at the top of the system are considered to be positive motivators that encourage the improvement of knowledge and skills through meaning and various incentives, whereas the elements at the bottom of the system are considered negative motivators that encourage bad emotion and should be minimized when planning and implementing the system (Bernik, 2021) 6. Andrade et al. (2016) A framework for intelligent gamification (FIG) structured in three layers: gamification layer, tutor layer and data layer. ...
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In recent years, university teaching methods have evolved and almost all higher education institutions use e-learning platforms to deliver courses and learning activities. However, these digital learning environments present significant dropout and low completion rates. This is primarily due to the lack of student motivation and engagement. Gamification which can be defined as the application of game design elements in non-game activities has been used to address the issue of learner distraction and stimulate students’ involvement in the course. However, choosing the right combination of game elements remains a challenge for gamification designers and practitioners due to the lack of proven design approaches, and there is no one-size-fits-all approach that works regardless of the gamification context. Therefore, our study focused on providing a comprehensive overview of the current state of gamification in online learning in higher education that can serve as a resource for gamification practitioners when designing gamified systems. In this paper, we aimed to systematically explore the different game elements and gamification theory that have been used in empirical studies; establish different ways in which these game elements have been combined and provide a review of the state-of-the-art of approaches proposed in the literature for gamifying e-learning systems in higher education. A systematic search of databases was conducted to select articles related to gamification in digital higher education for this review, namely, Scopus and Google Scholar databases. We included studies that consider the definition of gamification as the application of game design elements in non-game activities, designed for online higher education. We excluded papers that use the term of gamification to refer to game-based learning, serious games, games, video games, and those that consider face-to-face learning environments. We found that PBL elements (points, badges, and leaderboards), levels, and feedback and are the most commonly used elements for gamifying e-learning systems in higher education. We also observed the increasing use of deeper elements like challenges and storytelling. Furthermore, we noticed that of 39 primary studies, only nine studies were underpinned by motivational theories, and only two other studies used theoretical gamification frameworks proposed in the literature to build their e-learning systems. Finally, our classification of gamification approaches reveals the trend towards customization and personalization in gamification and highlights the lack of studies on content gamification compared to structural gamification.
... Despite the revealed positive impact of gamification on education, some studies showed negative effects on its use (Andrade et al., 2016;Mekler et al., 2013). This might be because learners have different individual characteristics and because of that they behave differently in computer-based learning (Essalmi et al., 2015;Tlili et al., 2016). ...
Gamification has gained an increasing attention from researchers and practitioners in various domains including education as it can increase learners’ engagement and motivation. However, little is known about how educational gamification experiences can be influenced by learners’ characteristics. Therefore, this study provides a systematic meta-review of empirical studies related to learners’ characteristics and educational gamification experiences. The obtained results from the meta-analysis of forty related articles are: (a) learners’ psychological and behavioral outcomes were affected by learners’ characteristics in educational gamification systems; (b) quantitative methods, using questionnaires, are the most used method to measure the effect of learners’ characteristics on their learning outcomes in educational gamification systems, and this needs to be changed to the new potential of using educational big data and learning analytics approaches; (c) personality traits is the most investigated characteristic followed by player types, but there is a need to further investigate other important factors of learners’ characteristics, such as working memory capacity and age; and, (d) a set of game design guidelines that should be taken into consideration while designing educational gamification catering individual difference were proposed.
... While the aforementioned literature highlights the benefits of SGs for various life domains, others have identified the dual consequences of applying gamification for learning (Andrade et al., 2016). Non-cognitive skills aside, some scholars have questioned the effectiveness of computerassisted learning. ...
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Serious games (SGs), are gaining prominence as a tool for early education at home as well as in school settings. Given the mixed effects of gamification on various aspects of users' lives, it is pertinent to study its broader effects on a child's preschool and school years. Given the lack of consensus on a comprehensive measure that encapsulates these effects on an individual's routine functioning, the present study examined whether various engagement states in SGs use influence a relatively broader measure of users' functioning across significant life domains such as Quality of Life (QoL). It is argued that it would serve scholars, teachers, and parents better to understand the broader implications of SGs on children's overall QoL rather than isolated physiological and behavioral effects. Consequently, utilizing structural equation modeling, results from 335 parents of 2-10-year-olds in a developing country showed that cognitive and behavioral engagement in gamified applications appear to influence the child's QoL, but not affective engagement. Results are discussed in terms of the consequences of using game-based technology for a child's development, with far-reaching academic, personal, physical, and social implications not only for the school-going ages, but also for early teenage years. The results are promising in relation to QoL. The findings indicate the role modern technology plays in improving individuals' lives. The findings provide scholars, parents, and creators of SGs important information for their plan of action regarding children's exposure to SGs and making SGs a frequent aspect of the learning experience early in life.
Gamification has captivated people's interests from all walks of life, including marketing managers. Incorporating game mechanics in a non-gaming context like social media marketing, email marketing, customer relationship management, e-commerce, and mobile marketing enhances customer engagement and loyalty. However, every coin has two sides, and the buzzword gamification is no exception. Marketing scholars have extensively explored the positive aspects or bright sides of using a gamified approach to marketing; hitherto, its lesser attended side or dark side deserves attention. This chapter focuses on the dark side of using gamification in interactive marketing. The discussion of the dark side revolves around three key themes: Design-based challenges in gamification, Challenges in adopting gamified marketing solutions (pre-implementation, during implementation, and post-implementation), and User-based issues leading to lesser or no impact of gamification in the context of online marketing. The originality of the work lies in providing a framework describing the dark side of gamification in online marketing.KeywordsDark sideGamificationOnline marketingSocial media marketing
Gamification with various designs is becoming a mainstay of interactive marketing, used to pervasively and holistically to in value-creating marketing practices. Beyond marketing, gamification is commonly seen as a technology, the effects of which are benevolent and which is often employed for sustainable ends such as the improvement of wellbeing, health, and sustainable work. However, as gamification commonly, either more or less directly, is related to attempts at affecting customers’ psychological states and continued engagement, a critical reflection of the ethical ramifications of gamification is crucial. Hazards such as manipulation, exploitation, psychological distress, and conflicts with cultural norms are considered as potential challenges that should be observed. Nevertheless, there is a current lack of examination of gamification’s ethical implications in the marketing context. In this chapter, the authors explore the ethical concerns related to using gamification as an interactive marketing tool, and examine how consumers shape their ethical judgement towards gamification. The authors also suggest various ways to help marketers, designers, and policymakers to minimize the unethical consequences of gamification, and ensure that companies will use gamification to compete both ethically and responsibly.KeywordsGamificationGameEthicsManipulationSustainableService
Twenty-first century societies are increasingly connected thanks to new technologies and the use of English as a lingua franca. This means that students have to be proficient in all four English language skills (writing, reading, listening and speaking) in order to obtain a university degree and get a job. Despite the fact that English is studied in Spain from a very early age, it seems that learners fail to acquire English language proficiency. Therefore, the main objective of this article is to investigate the effectiveness of gamification as an active methodology for teaching English, specifically to students of the BA in Translation and Interpreting at the University of Malaga (Spain). To this end, we have employed a mixed methodology to collect quantitative and qualitative data on the opinions of a sample of students of the mentioned degree. The results demonstrate that gamification has many positive effects on learning, as it enables students to be more motivated in the classroom.
Dada la importancia de adquirir estrategias metacognitivas que permitan aprender de forma autónoma, y que están presentes en todo el proceso de enseñanza-aprendizaje, el propósito de este libro es proponer herramientas metodológicas innovadoras que posibiliten mejorar el desarrollo autónomo del estudiante en el proceso de enseñanza-aprendizaje. El libro se estructura en cuatro capítulos. En el primero se exponen aspectos esenciales de la enseñanza y el aprendizaje en la educación superior, con énfasis en las características del aprendizaje activo y la enseñanza con ayuda de la tecnología. En el segundo se detallan múltiples estrategias de enseñanza y aprendizaje. En el tercero se describen los métodos de enseñanza en la educación superior, metodologías de educación virtual y metodologías innovadoras para potenciar el aprendizaje activo. Finalmente, se analizan dos casos prácticos sobre la aplicación de metodologías innovadoras y sus resultados en la Universidad Técnica de Babahoyo.
Research in learning technologies is often focused on optimizing some aspects of human learning. However, the usefulness of practical learning environments is heavily influenced by their weakest aspects, and, unfortunately, there are many things that can go wrong in the learning process. In this article, we argue that in many circumstances, it is more useful to focus on avoiding stupidity rather than seeking optimality. To make this perspective specific and actionable, we propose a definition of stupidity, a taxonomy of undesirable behaviors of learning environments, and an overview of data-driven techniques for finding defects. The provided overview is directly applicable in the development of learning environments and also provides inspiration for novel research directions and novel applications of existing techniques.
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Technology acceptance is essential for technology success. However, individual users are known to differ in their tendency to adopt and interact with new technologies. Among the individual differences, personality has been shown to be a predictor of users’ beliefs about technology acceptance. Gamification, on the other hand, has been shown to be a good solution to improve students’ motivation and engagement while learning. Despite the growing interest in gamification, less research attention has been paid to the effect of personality, specifically based on the Five Factor model (FFM), on gamification acceptance in learning environments. Therefore, this study develops a model to elucidate how personality traits affect students’ acceptance of gamified learning environments and their continuance intention to use these environments. In particular, the Technology Acceptance Model (TAM) was used to examine the factors affecting students’ intentions to use a gamified learning environment. To test the research hypotheses, eighty-three students participated in this study, where structural equation modeling via Partial Least Squares (PLS) was performed. The obtained results showed that the research model, based on TAM and FFM, provides a comprehensive understanding of the behaviors related to the acceptance and intention to use gamified learning environments, as follows: (1) usefulness is the most influential factor toward intention to use the gamified learning environment; (2) unexpectedly, perceived ease of use has no significant effect on perceived usefulness and behavioral attitudes toward the gamified learning environment; (3) extraversion affects students’ perceived ease of use of the gamified learning environment; (4) neuroticism affects students’ perceived usefulness of the gamified learning environment; and, (5) Openness affects students’ behavioral attitudes toward using the gamified learning environment. This study can contribute to the Human–Computer Interaction field by providing researchers and practitioners with insights into how to motivate different students’ personality characteristics to continue using gamified learning environments for each personality trait.
Conference Paper
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The development and use of Virtual Learning Environments (VLE) has increased considerably over the past decades. Following that trend, many research findings have shown the benefits of using VLE during the learning process. Nevertheless, there are important problems that hinder their use requiring further investigation. Among them, one of the main problems is the inappropriate use of these systems by students. The boredom, lack of interest, monotony, lack of motivation, among other factors, ultimately causes students to behave inappropriately and lead them to a lower performance. In this context, the proposed study investigates whether it is possible to reduce undesirable behaviors and increase performance of students through the use of game mechanics (i.e. gamification). We develop a VLE, E-Game, that can turn on/off several game mechanics, such as points, badges, levels and so on. A case study was conducted with two groups of students to investigate their behavior during their interaction with E-Game with and without gamification. The results indicate that the gamification implemented by E-Game contributed to improve student performance in the case of boys. Yet, improvement was not observed in the case of girls. Furthermore, it was not possible to conclude whether the use of gamification helps to prevent inappropriate student behavior, and therefore, further studies and experiments are needed.
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Flow is the affective state in which a learner is so engaged and involved in an activity that nothing else seems to matter. In this sense, to help students in the skill development and knowledge acquisition (referred to as learners’ growth process) under optimal conditions, the instructional designers should create learning scenarios that favor the learner’s flow. One-way to obtain these scenarios is to incorporate the conditions postulated by the Flow Theory in the context of the instructional design process. However, little attention has been drawn to this integration, and how to apply the Flow Theory based on learning theories that provide theoretical justifications during the instructional design process. Thus, in this paper, we propose a framework to integrate the learner’s growth process and the Flow Theory. It provides adequate support for the instructional design of learning scenarios that lead and maintain learners in flow state. We demonstrate the usefulness of this framework by presenting an application that helps designers to search and select learning objects that have the potential to maintain the learner’s flow in a learning scenario.
Conference Paper
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We propose a scaffolding system that provides adaptive hints using a probabilistic model, i.e., item response theory (IRT). First, we propose an IRT for dynamic assessment, whereby learners are tested under dynamic conditions of providing a series of graded hints. We then propose a scaffolding system that presents adaptive hints to a learner according to the estimated ability of IRT from the learner response data. The system provides hints so that the learner’s correct response probability is 0.5. It decreases the number of hints (amount of support) automatically as a fading function according to the learner’s growth capability. We conducted some experiments with students. The results demonstrate that the proposed system is effective.
Conference Paper
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Many learning environments are quickly deserted by the learners, even if they are efficient. Gamification appears as a recent game-based learning approach to enhance the learners’ motivation. The difficulty with this approach is that people have various expectations from games, and react differently face to specific game mechanics. In order to adapt the game mechanics of the developed game elements, we propose a player model complementary to existing learner models. This model aims to predict to which game mechanics the user is responsive, and is used to adapt the gamified features of the system.
Conference Paper
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The use of Computer-Support Collaborative Learning (CSCL) scripts is an effective approach to support meaningful interactions and better collaborative learning (CL). Unfortunately, in some situations, scripted collaboration decreases the motivation and engagement of students, which makes more difficult to use it over time. To deal with this problem, we propose the use of gamification as Persuasive Technology (PT) to induce the students to follow the intended learning behavior specified by CSCL scripts, with a positive change in the learners' attitude. Nevertheless, to achieve this goal, it is necessary an exhaustive knowledge on gamification and its impact on CL. Thus, we are developing an ontology to provide a formal systematization of the knowledge on gamification and its proper application in CL scenarios. In this paper, we focus in the formalization of basic concepts related to gamification as a PT in CL scenarios. Furthermore, to demonstrate the applicability of our approach in CL scenarios, we present a case study, where we built and apply a personalized gamification model based on the ontological structures defined in this work.
Conference Paper
E-learning has been used to help students in learning. But nowadays the problem in e-learning is the student's motivation and engagement. A study about engagement in e-learning said, students who rarely use e-learning get worse score than their friends who often use e-learning. A lot of methods have been used to motivate and improve student's engagement in using e-learning. One of the methods is by using social engagement in Web 2.0 technology, where students can be interactive with each other and participate in making lecture material. This function implemented in e-learning is called e-learning 2.0. The other method is by implementing game mechanism and elements in non-game applications like e-learning, called gamification. This paper explores related works on e-learning 2.0, gamification model, and then making a conceptual framework design, based on social engagement in Web 2.0 technology and gamification using Design Science Research Model as methodology. Using this framework design can be a guideline to people who want to implement gamification and Web 2.0 technology in e-learning system.
Intrinsic and extrinsic types of motivation have been widely studied, and the distinction between them has shed important light on both developmental and educational practices. In this review we revisit the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory. Intrinsic motivation remains an important construct, reflecting the natural human propensity to learn and assimilate. However, extrinsic motivation is argued to vary considerably in its relative autonomy and thus can either reflect external control or true self-regulation. The relations of both classes of motives to basic human needs for autonomy, competence and relatedness are discussed.