Reward-based Intermittent Reinforcement in Gamification for
E-learning
Sheng Luo, Haojin Yang and Christoph Meinel
Hasso Plattner Institute (HPI), D-14482 Potsdam, Germany
Keywords: Reward-based, Intermittent Reinforcement, Gamification, E-learning.
Abstract: Nowadays gamification is a hot topic in the world, a lot of websites, applications and researches adapt this
method to arouse users' motivation. From the past experience, gamification indeed has a positive influence
on users' motivation especially in e-learning field. However, the gamification method either is hard to be
applied to professional content called meaningful gamification or is negative on user's intrinsic motivation
called reward-based gamification. So we study the game addiction mechanism and propose the reward-
based intermittent reinforcement method in gamification to take advantage of user independence feature in
the latter one and eliminate the negative influence on user's intrinsic motivation. In order to investigate the
practicability and integrate effectiveness, we implement this model in our tele-teaching platform.
1 INTRODUCTION
Gamification is growing rapidly and becomes a
important tool in various areas since it appears in
2010. In last four years, it has been applied to a lot
of scenes like education, work and so on.
Researchers and engineers utilize its advantages to
sustain the existing users and attract the new. Figure
1 shows the search result from Google scholar
search engine.
Figure 1: Searching result of gamification.
As showed in figure 1, the number of researches
about gamification is increaing rapidly. In 2010,
there is only one search result which includes
"gamification" in its title. However, this number is
as high as 1090 only after 4 years. This rapid growth
shows that more and more researchers begin to
utilize gamification in their works.
Additionally, gamification is not just research
and theory, it also has been integrated into a lot of
platforms, especially the e-learning platform. For
example, "Codecademy" (e.g Learn to code, 2015) is
a website to learn programming. It takes full
advantage of gamification to make learning code
funny and provide a new learning experience for
learning code. Moreover, Hamari et al. (2014) prove
the positive effect of gamification from lots of
researches about gamification, it is that gamification
can bring higher engagement and enjoyment in
various contexts. In gerneral, past experience proves
the advantages of gamification for e-learning.
Our research is based on our e-learning platform
"tele-TASK". tele-Task (Schillings and Meinel,
2002) is an integration solution for recording
lectures and presentations, post-processing and
publishing them on the internet as shown in Figure 2
(Tele-TASK: More than video!, 2015). It contains
several parts which are recording, live streaming and
archive. In archive part, portal, iTunes U and mobile
website are used for publishing our lectures. The
target of tele-TASK is recording lectures, seminars,
conferences or any professional videos. Users are
mainly doing self-learning and after-class learning in
our context. They belong to individual learning
which is different from MOOC that has time-
limitation or many users are learning in the same
time. So they have higher requirement for users'
motivation, engagement and conscientiousness. In
177
Luo S., Yang H. and Meinel C..
Reward-based Intermittent Reinforcement in Gamification for E-learning.
DOI: 10.5220/0005402201770184
In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 177-184
ISBN: 978-989-758-107-6
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
addition, that the content of our platform are
professional or theory courses means higher
difficulty and less enjoyment. Consequently, users in
our platform are more easier to be disturbed and the
dropout rate is higher.
Figure 2: Tele-task workflow.
Our research mainly aims at utilizing the
advantage of gamification to increase users'
motivation, engagement and the enjoyment of
learning in our tele-TASK learning platform. The
main research question related to our target are: (1)
What is the state of the art gamification method? (2)
What are disadvantages and advantages of those
methods? (3) Which method is most suitble for our
platform? (4) How to optimize this method for a
better outcome?
The rest of this paper is organized as follows.
Section 2 gives a brief introduction of existing
gamification researches concerning on learning and
the theory foundation of our research. In section 3,
we explain the implementation of common reward
gamification in our portal. Section 4 discusses the
model of reward-based intermittent reinforcement in
gamification. Conclusion and future plan can be
found in Section 5.
2 RELATED WORK AND
THEORY FOUNDATION
Utilizing game in learning contexts has a long
history. In the first place, researchers used the
method of designing a game to design a learning
course. This method is game-based learning
(Prensky, 2003). After that, Shih, Squire et al. (2010)
analyze the research trends of the information and
communication technologies for game-based
learning. Lau et al. (2014) discuss the research
challenges and future trends of latest e-learning
specific multimedia technologies, and one of those
potential research directions is gamification.
Figure 3 shows that game-based learning and
gamification, they both are methods to combine
Figure 3: Relationship between game-based learning,
gamification, reward-based gamification and meaningful
gamification.
game with learning. Gamification consists of
reward-based and meaningful gamification. Here
system and character mean the whole game system
and character of game system, respectively. It is
obvious that the difference between game-based
learning and gamification is the utilization
percentage of game. Game-based learning is a game
with learning contents, while gamification is
learning course with game elements.
In Figure 3, gameful means rules and
competition or strife or goals. Playful means
improvisation, expressiveness, spontaneity, and joy
(Lucero et al., 2014). Here meaningful gamification
is one typical kind of playful gamification design
method. Reward-based gamification is utilizing the
game rules and relying on the extrinsic rewards,
while meaningful gamification is utilizing the joy of
game and relying on intrinsic rewards. So gameful
and playful are the main difference between those.
The definition of these methods can be find in
(Prensky, 2001), (Deterding et al., 2011) and (MIT
Game Lab, 2013). From these definitions, it is
obvious that game-based learning is not suitable to
be applied in our context. Therefore the remaining
potential methods are reward-based gamification and
meaningful gamification.
Meaningful gamification is first proposed by
Prof. Scott Nicholson in (Nicholson, 2012). After
that, he applies it to classroom management
(Nicholson, 2013) and proposes six concepts about
meaningful gamification (Nicholson, 2015). Beside
meaningful gamification, some other researchers
propose to use playful design in gamification like
(Deterding et al., 2011) (Lucero et al., 2014). Based
on this meaningful or playful design, intrinsic
motivation which is very helpful for learning can be
aroused. However, it only has been applied to
primary courses or activities but no professional
courses. Higher difficulty and less playful in
professional course are the main reasons. That is to
CSEDU2015-7thInternationalConferenceonComputerSupportedEducation
178
say, this meaningful design or playful design is not
applicable too.
The remaining gamification method is reward-
based gamification which is the mainstream in
gamification researches. Hakulinen et al. (2013)
prove that achievement badges can be used to affect
the behaviour of students even when the badges
have no impact on the grading. Iosup and Epema
(2013) prove that gamified courses show a high ratio
of students who pass after the first attempts. In the
summary made by Hamari et al. (2014), reward-
based gamification in education are positive.
Especially, one online learning platform has been
reviewed in (Hamari et al., 2014), and the result
shows that reward-based gamification has a positive
impact on time management, carefulness and
achieving learning goals.
However some researchers argue that reward-
based gamification is not a suitable method because
extrinsic reward has negative influence on the
intrinsic motivation (Deci et al., 2001). Those who
perform some activities because of rewards will be
less motivated when the reward is moved. So the
long term things like behaviour change and learning
should adapt other method to call forth the intrinsic
motivation.
In summary, reward-based gamification is
suitable for our context, it can help to arouse users'
motivation, but it also has negative influence on
long-term learning activities. So in our research, we
focus on adapting the advantages of reward-based
gamification and proposing a new method in reward-
based gamification for sustaining the motivation and
engagement of long-term users. In the first place, we
import the common badge system into our platform,
that is a general thing which you can find in lots of
gamified websites. The next is our reward-based
intermittent reinforcement model in gamification.
The goal of this model is keeping long-term users'
motivation and engagement. The theory basic comes
from game addiction theory.
Game is easy to attract users' motivation and
engagement because it is a game. But not all games
can be played for a long time, some games are
specially attracted, some aren't. In game design, lots
of factors affect the quality of a game, like graph,
music, story, additive mechanism and so on. There is
no standard for every factor, but user must get some
positive stimulations from the good element.
The elements in game design can be divided into
two kinds, one is user-related; the other is user-
independent. Elements like reward, badge, leader
board are independent of user. Elements like story,
music are related to user. Because everybody has his
own idea even for one thing, it is impossible to
design a element which everyone likes. Because of
independence, the positive stimulation of user-
independent element is more direct and it can attract
more users than user-related elements do. Game
addiction mechanism which is also user-independent
element is the rule of positive stimulation occurring.
A good game addiction mechanism can amplifier the
effect of positive stimulations. Then player will be
attracted by this game which also turns into a good
game. So a good game must be a addictive game.
(Flappy Bird, 2014) is good example about addictive
game. It only has a good addiction mechanism,
beside the addiction mechanism, nothing in this
game can be thought as a good design.
Prof. Bennett Foddy from Oxford explained the
main addiction mechanisms in game design. The
first is immediately feedback that a gaming
experience is more addictive if it has shorter latency
between reward and action (Ethics and Addiction in
Games – Develop Conference, 2012). The second is
intermittent reinforcement. The third addiction
mechanism is the diminishing reward which means
improve the difficulty of getting a reward step by
step.
The first addiction mechanism actually is used in
lots of gamified websites and our platform. That is
the common reward method in common badge
system. Simple award rules bring shot latency, so it
can help to arouse users' motivation. But it has
negative influence on users' intrinsic motivation.
The second and third addiction mechanism are
applied in our platform by a intermittent
reinforcement based on diminishing reward. We
improve the initial difficulty of getting next badge,
but whether the user can get a badge is completely
random. There is a experiment about intermittent
reinforcement. Rats are given a button that provides
food on different schedules – every time it’s pressed,
every tenth time, or randomly. The result is that the
rat is far more likely to compulsively hit the button
if it’s on a random schedule. The intermittent
reinforcement can be found in the slot machines, that
why it has been popular for so many years. From the
psychology experiment (Cameron and Pierce, 1994)
(Hogarth and Villeval, 2010), intermittent
reinforcement not only can lead to more persistence
and higher total effort but also won't have any
negative influence on users' intrinsic motivation. In
addition, the random intermittent reinforcement also
won't make users addicted.
In summary, the common badge system can
rapidly arouse uses' motivation in the beginning,
after that, the intermittent reinforcement takes
Reward-basedIntermittentReinforcementinGamificationforE-learning
179
responsibility for sustaining users' long-term
motivation and intrinsic motivation.
3 COMMON REWARD MODEL
IN TELE-TASK
Figure 4 shows the homepage of tele-TASK portal
which includes more than 5300 e-lectures, 19000
podcasts, 1900 lecturers and 420 collections (Bauer,
2015). It is the main video publishing window of
tele-TASK. The biggest ratio of users are those who
learn our courses after class or by themselves. And
most of these videos are our professional courses
and research seminars. Therefore, how to improve
the engagement of users in our platform is our
research key point.
Figure 4: Homepage of tele-TASK portal.
In order to improve the learning efficiency, we
provide several learning tools for our users. Figure 5
shows the main tools in our portal. Those are
tagging, marker, note (manuscript), rating and link.
Besides, we have playlist and group tools for
collaborative learning. The detailed function of those
tools can be found in (Moritz et al., 2010) (Siebert et
al., 2010).
In our model, the activities that users use
tagging, maker, manuscript, rating, link, playlist are
considered as effective learning activities. Our
model monitors these activities to award users
badges for arousing their motivation and keeping
their engagement. So the effective learning activity
is the foundation of our reword model. The approach
in our system is awarding users based on the number
of effective learning activities. For example, the
model awards a level-0 tagging badge to user who
tags 10 taggings, a level-1 tagging badge to user
who tags 100 taggings.
The reason why we first import the common
reward model into our system is its simple rule.
Therefore reward is easy to be found by users thus
Figure 5: Learning tools in tele-TASK portal.
Figure 6: Main achievement page.
inspire users at first. In game addiction mechanism,
the common reward model is the immediately
feedback, which is the easiest way to arouse users'
motivation and engagements.
Figure 6 shows the main achievements page of
user. This page shows all badges obtained by one
user in time order. There are several filters above all
badges, those are used for showing the badges in one
certain aspect.
Figure 7: Badges in one aspect.
Figure 7 shows the certain badge page which
contains all badges from low level to high level in
one certain aspect and its current progress. The
progress function gives the user a clear brief of their
badge status. So it is a good assistance for our
intermittent reinforcement reward which is a random
mechanism. Because of the random feature, users
can't know that if there is no hint. But the progress
CSEDU2015-7thInternationalConferenceonComputerSupportedEducation
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bar provides a good hint for intermittent
reinforcement reward. Once they know this badge,
the intermittent reinforcement reward just like a slot
machine in our portal.
Figure 8: Leader board.
Figure 8 shows the leader board in our badges
system, the leader board has different time slots.
There are three different time slots which are last 7
days, last 1 month and whole time respectively. New
user can easily enter the leader board based on short
time slot. Therefore, shot-term leader board is
designed for arousing the motivation and
engagement of the new user. The leader board of
whole time is based on the all badges in our system,
so it won't change so quick and is used for sustaining
the motivation of old user.
These above methods are designed for users who
like badges or like to compare with others, but not
all users like that, especially, some users don't like,
and even worse, hate this comparison. Actually, we
conduct a survey which contains a question about
gamification, more than 20% users aren't interested
in gamification or dislike gamification. So in our
design, we also consider the requirement from this
part of users, no matter badges or leader board, users
have the right to turn it off if they want. But the turn-
off function of leader board is only valid for the user
who is already log-in. Regarding visiting user, they
have no right to turn it off.
The section above is the common method in our
system. Lots of websites have this common reward
system which may have different patterns of
manifestation. But the theoretical foundation, that is
just like that we use candy to persuade children to
try some new things, is same. The goal of common
method is arousing users' motivation at first sight.
When people have no idea about content, reward is
the most direct positive stimulation for them, so they
will be attracted by badges. But this common
method just has a good effect on new user, because
it uses a very simple calculation method to award
users. That is "You do it , I award you". Therefore
once users know this rule, it will be less attractive
than at the beginning, and then reward can't arouse
users' motivation and engagement anymore. It is
necessary to propose another method for sustaining
our users' motivation for a long time. The next part
will explain this solution in detail.
4 REWARD-BASED
INTERMITTENT
REINFORCEMENT MODEL
In this chapter we will explain our method to sustain
long-term users' motivation. As we explain in
chapter 3, addiction mechanism is a very important
to the quality of game. It decides the effectiveness of
positive stimulation in game. Here we adapt the
intermittent reinforcement and diminishing reward
to keep users' motivation. The implementation detail
is described as follows:
Model monitors all effective learning
activities.
Model calculates the probability of gaining
points after find a effective learning activity.
Three variables that we will explain later
contribute to the probability result.
A random number is generated by model for
comparing with the probability result.
Comparison result decides whether to add
user's random badge point.
If user's point increases, then the failure times
will be cleared. Progress will increase and our
model will check the point whether it meets
the requirement of next level. If comparison
result is negative, the failure times will
increase by 1.
Every new level badge is awarded, the badge
number will increase, which has an effect on
the basic probability.
This probability is calculated by the opportunity
calculation function which is showed in function (1):
f0.3
6
x
6
0.4
y
y
15
0.3
1z
(1)
x∈
0,4
y
∈
0,∞
z ∈ 0,1
(2)
Here X means the number of badges which user
owns, Y means the failure times, Z means the
progress to next badge. (2) shows the domain of
these three variables. So the range of F is from 0 to 1.
Reward-basedIntermittentReinforcementinGamificationforE-learning
181
The domain of sub-functions can be found in the
next.
f1 0.3
6
6
∈ 0.3,0.257,0.18,0.12,0.099
(3)
Function (3) is the first sub-function of our
calculation method. Now we only have five levels
for this badge, so the number of X can only be
0,1,2,3,4. Then the basic probabilities of this sub-
function are 0.3, 0.257, 0.18, 0.12, 0.099. It is
obvious that the success probability is decreasing
with the increasing of badge number. That is to say,
the difficult of gaining another badge is directly
proportional to badge level.
Figure 9 shows the probability curve of function
(4) which is based on failure times (y). It is obvious
that this function is a increasing function and the
maximum of the function is 0.4. When design this
function, we want it increase slowly in the beginning,
while it increases faster and faster with the
increasing of failure times. This design can bring a
suitable degree of difficulty.
Figure 9: Probability curve of failure times.
2 0.4
15
∈0,0.4
(4)
Figure 10: Probability curve of progress.
3 0.3
1
∈ 0.3,0
(5)
Figure 10 is the probability curve of progress as
shown in function (5). Here Z means the progress to
next badge. This design also adapts diminishing
reward concept, it can bring a good influence on
users' motivation.
In function (1), when x equals 0, function (1)
equals function (6).
f 0.30.4
y
y
15
0.3
1z
(6)
Figure 11: Probability plate when badge number equals 0.
Then the function (6) is shown in Figure 11. In
this figure, X, Y, Z individually stands for the
probability F, failure times and progress. Figure 11
gives a direct explanation of how failure times and
progress affect the probability. In addition, when
failure times is 0 and progress is close to 1, the
probability is 0.3. While the progress is close to 0
and failure times is 100, the probability is
0.9478260869565.
P
0.3
6
6
0.4
15
0.3
1
1
0.3
6
6
0.4
15
0.3
1
(7)
∞
9
15
∗
6
∗14!
14
!
(8)
We assume that the badge number is "a",
progress is "b". The total probability of awarding
user points is function (7). When a new user uses our
system, then both "a" and "b" are 0, so the
probability of gaining the first point can be
CSEDU2015-7thInternationalConferenceonComputerSupportedEducation
182
calculated by function (8). The result of function (8)
is close to 1.
In our model, high level of badge means more
points. Currently the required points for 5-level
badges are 100, 300, 800, 1900, 4200. The required
point to the next level is double of the required point
of existing level plus the next level multiplies 100.
Our model rises the difficulty with the rising of
badge numbers, and the progress is also based on the
difficulty rising model. The failure times in this
model is used for ensuring that user can gain the
badge at last after many tries. In one word, It is
harder and harder to get more badges, but it becomes
easier and easier with the rising of failure times.
That difficulty is related to user's level is
diminishing reward theory. While random number is
the one who makes a decision about if user can gain
the point. Fundamentally, it is intermittent
reinforcement. These two mechanisms work
together to arouse users' motivation and sustain
users' long-term motivation.
5 CONCLUSIONS AND FUTURE
WORK
In this paper, we propose a reward-based
intermittent reinforcement model in gamification for
e-learning. It consists of common reward model and
intermittent reinforcement model. The former
utilizes the number of effective learning activities to
award users. The latter one is based on the
probability calculated by user existing status. In our
model, these two models not only can arouse users'
motivation at the first place but also have a good
persistence for long-term learning. In addition, it is
easy to implement and has no concern to users'
background, experience and so on. In one word, this
model overcomes the shortcomings of reward-based
gamification and meaningful gamification, it is
particularly suitable for on-line learning platform
with professional contents.
The basic framework of reward-based
intermittent reinforcement model has been
implemented. But there are still a lot of
improvements need to be done in the future.
First of all, learning activity verify system is
needed to ensure the effectiveness of learning
activities. When evaluating our model, we find that
our learning activity system has a potential risk
which is the cheating from user. Our model
calculates every learning activity without
verification, if a user malicious chases a badge,
invalid operations will appear but still be counted
into model. For example, one will use the same text
for lots of notes or same tagging for lots of videos.
Therefore, we intend to use the operation time of
activity to verify the effectiveness of activity. That
the duration between two activities is too short
means user must be cheating now. But different
activity should have different time slot. If there is
tagging on the video page, the tagging event is a
easy action which is only a click. The marker event
also occurs very close at a big probability. So the
time slots of these should be as small as that people
can do it twice in the best internet condition. While
the "Note" "URL" "Playlist" should cost a long
period of time, because user needs to type something,
interactive with database or click several buttons, so
the time slot can't be too narrow. Too big time slot
for "Tagging" and "Marker" and too narrow time
slot for "Link" "Playlist" "Note" are invalid, because
the former one will ignore the valid learning
activities, the latter will bring misrecognition of
invalid one.
Furthermore, using evaluation to bring a better
reinforcement result. The common reward method of
gamification is obvious effective for e-learning. But
the effectiveness of reward-based intermittent
reinforcement model and the parameters of this
model need long-term experiment to verify and
optimize. Unfortunately, there is no accuracy
mathematic model for the difficulty model in game
addiction theory. All difficulty model come from the
psychology experiment or experience which are not
precision, although our model is based on the theory
of game addiction mechanism, it doesn't mean that
our model is optimized in e-learning. So lots of
experiments are need for optimizing parameters,
making the system suitable for more users.
We intend to evaluate our method in our real
course. Two platforms will be provided to our
students in the same time. One is original portal
without gamification module, the other is testing
portal with gamification module. Every student
needs log in our platform with his own account.
After the end of this class, the effectiveness of
gamification can be verified by comparing the
number of users, the online learning duration, the
final score in these two platforms. Moreover, we can
get more information by designing a survey for our
platforms. Especially, the result from the users who
pay less attention on our platform can help us a lot to
improve the universality of our gamification method.
The parameters of our reward-based intermittent
reinforcement model can be evaluated in the same
way which is comparing the result from platform
Reward-basedIntermittentReinforcementinGamificationforE-learning
183
with reinforcement model and without reinforcement
model in real course. Once or twice evaluation
experiment can bring optimized parameters for our
platform.
At last but not least, extending the system for the
users who are not interested in gamification or
reward. There are three kinds of users whom our
existing model can't motivate. They are users who
have a clear learning target, have no interests in our
reward or in gamification. The first kind of users
don't need any extrinsic motivation to arouse their
motivation, they have enough intrinsic motivation.
While the second kind of users need us provide more
kinds of reward to inspire them. We have the close
option for the third kind of users, but that is not
enough. We can try to express our content or
platform with more creative and interesting way or
build some playful learning assistance tool to attract
these users.
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